{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Autoencoder\n",
    "\n",
    "## Introduction\n",
    "\n",
    "讓我們仔細來看一下之前所實作的 Autoencoder 的網路結構，不管它的 `encoder` 還是 `decoder` 都是 `fully connected` 的結構．那就會有一個問題是如果網路的結構換成 `convolutional` 的樣子，是不是同樣可以 work 呢?答案是可以的，也就是今天要來看的 `convolutional autoencoder`．\n",
    "\n",
    "在 CNN 中，主要有兩個部分一個是 `convolutional layer`，另一個是 `max pooling layer`．在 autoencoder 的 encoder 以及 decoder，fully connected 的結構都是相對應的，例如 encoder 中第一層是 784 維降到 300 維，則相對的在 decoder 中的最後一層就要是 300 維升到 784 維．因此如果在 encoder 的部分有 convolutional layer，則在 decoder 的部分就要有一個 `deconvolutional layer`；在 encoder 的部分有 max pooling layer，則在 decoder 的部分就要有一個 `max unpooling layer`．\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Packages loaded\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from libs.utils import weight_variable, bias_variable\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot = True)\n",
    "print(\"Packages loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deconvolution\n",
    "\n",
    "那在 encoder 中的 deconvolution 要怎麼做呢，以下有一個簡單的 gif 例子，而在 tensorflow 的實現上已經有了一個 [tf.nn.conv2d_transpose](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.conv2d_transpose.md) 來讓我們直接使用．\n",
    "\n",
    "![](http://imgur.com/J3uW7A7.gif)\n",
    "\n",
    "這裡會建立一個包含兩層 encoder 以及兩層 decoder 的 convolutional autoencoder 來試試看它重建輸入的能力如何，而這裡的 strides 我們會設定成 2，也就是說對一個 mnist 輸入影像 28 * 28 維，經過 convolutional layer 之後會變成 14 * 14 維．達到維度降低的效果．以下是各層輸出的維度比較．\n",
    "\n",
    "- x 維度: 28 * 28，channel: 1\n",
    "- encoder layer1 維度: 14 * 14，channel: 16\n",
    "- encoder_layer2 維度: 7 * 7，channel: 32\n",
    "- decoder_layer1 維度: 14 * 14，channel: 16\n",
    "- decoder_layer2 維度: 28 * 28，channel: 1\n",
    "- x recontruct = decoder_layer2\n",
    "\n",
    "(`tf.nn.conv2d_transpose` 的參數跟 tf.nn.conv2d 很像，只是要多一個 `output_shape`)\n",
    "\n",
    "### Build convolution and deconvolution function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding = 'SAME')\n",
    "\n",
    "def deconv2d(x, W, output_shape):\n",
    "    return tf.nn.conv2d_transpose(x, W, output_shape, strides = [1, 2, 2, 1], padding = 'SAME')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build compute graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code layer shape : (?, 7, 7, 32)\n",
      "reconstruct layer shape : (?, ?, ?, ?)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "x = tf.placeholder(tf.float32, shape = [None, 784])\n",
    "x_origin = tf.reshape(x, [-1, 28, 28, 1])\n",
    "\n",
    "W_e_conv1 = weight_variable([5, 5, 1, 16], \"w_e_conv1\")\n",
    "b_e_conv1 = bias_variable([16], \"b_e_conv1\")\n",
    "h_e_conv1 = tf.nn.relu(tf.add(conv2d(x_origin, W_e_conv1), b_e_conv1))\n",
    "\n",
    "W_e_conv2 = weight_variable([5, 5, 16, 32], \"w_e_conv2\")\n",
    "b_e_conv2 = bias_variable([32], \"b_e_conv2\")\n",
    "h_e_conv2 = tf.nn.relu(tf.add(conv2d(h_e_conv1, W_e_conv2), b_e_conv2))\n",
    "\n",
    "code_layer = h_e_conv2\n",
    "print(\"code layer shape : %s\" % h_e_conv2.get_shape())\n",
    "\n",
    "W_d_conv1 = weight_variable([5, 5, 16, 32], \"w_d_conv1\")\n",
    "b_d_conv1 = bias_variable([1], \"b_d_conv1\")\n",
    "output_shape_d_conv1 = tf.pack([tf.shape(x)[0], 14, 14, 16])\n",
    "h_d_conv1 = tf.nn.relu(deconv2d(h_e_conv2, W_d_conv1, output_shape_d_conv1))\n",
    "\n",
    "W_d_conv2 = weight_variable([5, 5, 1, 16], \"w_d_conv2\")\n",
    "b_d_conv2 = bias_variable([16], \"b_d_conv2\")\n",
    "output_shape_d_conv2 = tf.pack([tf.shape(x)[0], 28, 28, 1])\n",
    "h_d_conv2 = tf.nn.relu(deconv2d(h_d_conv1, W_d_conv2, output_shape_d_conv2))\n",
    "\n",
    "x_reconstruct = h_d_conv2\n",
    "print(\"reconstruct layer shape : %s\" % x_reconstruct.get_shape())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(tf.pow(x_reconstruct - x_origin, 2))\n",
    "optimizer = tf.train.AdamOptimizer(0.01).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, loss 0.103204\n",
      "step 100, loss 0.0235892\n",
      "step 200, loss 0.0167378\n",
      "step 300, loss 0.0203425\n",
      "step 400, loss 0.0175616\n",
      "step 500, loss 0.0171841\n",
      "step 600, loss 0.0155463\n",
      "step 700, loss 0.0153204\n",
      "step 800, loss 0.00345139\n",
      "step 900, loss 0.00248451\n",
      "step 1000, loss 0.00312759\n",
      "step 1100, loss 0.00264636\n",
      "step 1200, loss 0.00225495\n",
      "step 1300, loss 0.00223552\n",
      "step 1400, loss 0.00223038\n",
      "step 2000, loss 0.0017688\n",
      "step 3000, loss 0.00150994\n",
      "step 4000, loss 0.00099031\n",
      "final loss 0.000948159\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "batch_size = 60\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)\n",
    "\n",
    "for epoch in range(5000):\n",
    "    batch = mnist.train.next_batch(batch_size)\n",
    "    if epoch < 1500:\n",
    "        if epoch%100 == 0:\n",
    "            print(\"step %d, loss %g\"%(epoch, cost.eval(feed_dict={x:batch[0]})))\n",
    "    else:\n",
    "        if epoch%1000 == 0: \n",
    "            print(\"step %d, loss %g\"%(epoch, cost.eval(feed_dict={x:batch[0]})))\n",
    "    optimizer.run(feed_dict={x: batch[0]})\n",
    "    \n",
    "print(\"final loss %g\" % cost.eval(feed_dict={x: mnist.test.images}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot reconstructed images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_n_reconstruct(origin_img, reconstruct_img, n = 10):\n",
    "\n",
    "    plt.figure(figsize=(2 * 10, 4))\n",
    "\n",
    "    for i in range(n):\n",
    "        # display original\n",
    "        ax = plt.subplot(2, n, i + 1)\n",
    "        plt.imshow(origin_img[i].reshape(28, 28))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "        # display reconstruction\n",
    "        ax = plt.subplot(2, n, i + 1 + n)\n",
    "        plt.imshow(reconstruct_img[i].reshape(28, 28))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4qpSLxcITDDmnF9I9bPmV6NuB7PnQXm+sMuCbnZuNDnQtFgsMBgO0223c39/j\nr7/+wr/+9S/PXKVo9hiYSaQP8ayE0D6b7sWUdkwMBvNSaj0hjKfA+c8Sbd0QlhlSpVIJ+/1eDko6\nIGKVEG8TnenIxtO0luAjs9R1GX80Gn3S7wsEAiKEJZNJZLNZAJ/91Jn5NJvNMJ/Pxd6B5a4UZ5mh\nZ3vn60BbmbiWXExWYbmztno4h/4f+mLKYBoTXnQlBIPM7oXZAsg/B9pijPY16XTak0XsNi1nVRdF\niMFgIHsusybNvuY80AELBi0o3rPx+OXlpee1j0QiEsSaTCbSqLnRaOD+/h6fPn3Cx48f0W63T/3n\nHcD1TSdRXV5eSiVEJpORBKrNZiNJK41GA3d3d2i32yJC8N5ivAw6DqMDu9fX17i6usLV1RWur689\nFVoMwrksl0sRKFwxFficQW78fPg1gz7Wv0h/rJuj6/tAJpMRe2B3r/wSfrE0v9jblz7HRBMdC6KV\nDsdyuXz0c3J/PhNJuacbL8exRuU6KZODZ3i9zzORnvdi9inRg72Ogc/zq9VqHdgXTyYTTy/YcDiM\nxWLhqY49Np/PsRr27EQIfWih4k6vQbcSQlczuH76u91O1PlsNotisYjRaOSpnnCDyBzn8AZ3qzoY\nPNEWEwxm62Yl5zS5Xju0UZjNZpKd2Gw25aC1Wq2QyWQ8avdyucTDwwNub2/R6XQwHo+lXErbhj0W\nekZTlHM91bnwPJfPoWEA3swT7ROrLUQCgQC22y2WyyWm06lUoeleJLY+vS3C4TBSqZQnkEKhQYsO\nbjLAU6EIkUgkpK8D/53JZER0cA//k8kE3W5XsqMAYDabHfTlMc4PbQXHQzezibTtFwBpbrpYLKQn\nDUXRU72+OpFBV1XSRsCvJ4SutLR9/e2ie5zoijJmHBeLRRFtj1X3bDYbqXjt9/vodrsYDoeYTqfW\n/+uM0BmTDPBqu2EtOuleSXx9h8OhJDe1220Mh0PM5/OzqXoAPierMEiYSqWkb1ilUsHFxQXy+Tzi\n8bgkrOx2O/R6Pany4MesqLX+Tc+PK/K7a1G9XketVkO1WkWhUJCG6bqH0bH1KRgMSiJdtVrF9fU1\nYrGYZPPy0Szj3jY6TqEr67U/PoADu3Tte69tZ3RszK/Hq19A1hU9+PX65/s1/tXPx70z8NHt2/Oj\nKyHu7u4kpmSC7POjK/X12V33ctC9Dxlv1q4pOtnA7afj2jPpJGc+FgoFAJ+T/fL5vKd3ktsvgvPM\nbTvAvmE2xVydAAAgAElEQVR6PjJx/9ScrQiRz+dRrVZxc3ODfD7vsXZIp9MHC5VfT4jFYiECBDc6\nLVzwUSuXulT+lDDbXi943LBp6TObzTxVHafO+HtrMKBK/8rtdisercvlEuPxGMlk8sB/vNvtotls\nejaMpwa6mB3nihCpVOpAhAC+z9PQMI7hHhqPWYjwPUNriH6/LyKEXSbfHpFIBKlUCoVCQUQINqbm\nwUzPlW9pQu0HbQISiQT2+70E79Lp9MHBX2cjTSYTjwDBywD3TgAWrDtT+Jrr5m660SkP9e7lr9/v\nn0Uw1m1ex8sMxdxEIuHJbNKNqS254O2iz2zMPE6n0xL4u7i4EBEiFosdDfLxDsNMeV0JYSLE+aBF\nCDYfpwihRXuucdyrNpsNZrOZp8K60+lgMBjInfYc0IkqHBQh2DesVquJyMK+jfP53CNA9Ho9T/KK\nnRufH51ZzjMVX7darYabmxtUKhVJBKUwqs/+x/apUCjkiemMx2NEo1FJUmLPEx3HsVjG20M36mXP\nEQZh+ciKAp1oq8907rxwxQgdE6RFjR6uELLb7eRn00JJN/xljNDPQUV/jh9rhxb9/PW/n0qn0/Ek\nthrPi65a5L6tRQNawbqDAoR+dMUKJh7phE6/nhDAZwGCSXZu0rObRE+xS89D2sSzv4RbfXbKtfbs\nRAhuWIVCQVRzCg9skJRMJg+a+vKgrVUkvgh6gXFVTy1CuH69p4Ql/HowqDcYDCSjdLlcevzA7MLx\n42AlBEveufDrvg+xWOxgo9K9SCaTiW+lzmOhCEG/QzYDc0UIwAQI4/lwe0LowJnbTJWVEP1+X0QI\nCqa2Pr0t6GvNSoh37955Mr35sc4M+R6/fi1CcG10qxtpT6KzQ0ajkZS7ssnndrv1eO9bdtF54tov\nsbpVCxDpdBq73U7OcaPRSDLCTx2M9RMhmP3HtdTPjkk3p7aeEG8TXSWjs48vLi5EhKBtzbHz3bFK\niFPPe8OL7mekm4+7VYN6r2QlxGw2k0qIu7s7CSickwgBeIUWBm1037B6vS7JKvy7FouFR4Todrvo\n9/uylpsI8fy4AhKDXqVSCRcXF7i5uZE+JRy8f+rAsh+hUEiyeGu1GlarlZwPAch5jM/DenO+TbQI\noZMraU+Ty+VEmNS2RrPZTBJBeb53xQW/agjA6yiy2+18+5UsFguPfasWJTjc+4QOAusYo1/PWfdu\n8hTY45bD7irPj7uXsZrLTYp3+z1omyS3ekJX/bhiBee4hnfdbDbrERe0+OUnROgEPM7vdruNSCSC\n7XbrsQY7dc+5sxMhtAc+KyF4CGd2ZSwWe1RvBLesyq2eYMNAbdtwLiIEn5uedJ1OB8lkUoIpbLDJ\nv83U0R8Ls7opVIXDYY8AwdfCFbbo/cwFgYfop/Tw8LNj+pH2JobxGPwqIdxGc1YJ8fPBbEddCaEv\nG/rAr8v9nwozqHiJ0fu7vnC4WfHD4VAECAZ0uF9SgLCL73ni1wPCr7x5Pp+LEDEYDNDpdKQSgk1N\nT/X89eXbrxLCz47JbBbfLvq15dzws2Oi2PalSggGUihCWCXEeUIBnQENbcdEISKdTh/slW4lxN3d\n3YH1wjngJqrw3sKMetoxMVFrMpmIzZSfHZMOtti+/Lz4NfrVguj19TVKpZKnGlH3IuTP8EPbMTHw\nxUo/3hXC4bAnkdTWrLeHtmFicJfJlZVKBeVyGbvdTtw+ptOp2BAyqdivV5afbRLXRP3f9/v9gcUO\ng7iz2Qzj8ViEXTegqy1w/CxfmWTnupe4ljjfM69dQcN4XtzKRQpm5XJZRrFYFBGN/R7cpDudVOSK\ntl+7FycSCV/rL33v9etLq99D0+kU4/EYoVBI7kfD4dDz+6wSQuFmjekLmW4IDHhLsfi9ekPkYueW\nX7nlW67Kqb2y9IvzIzPO3Z/v/mxXhFitVtJck5/jhq6DKMaPg/ONqjoFCWad8WLoLgxakNDf/zXc\n4B09VVOplARbstmsBC0ikYjHO1oPvWFpFf4pfSkMgxdoWt+wOk3PQ65DVN61JzvfOzbvXi96T+Xe\nrA9g7Nv0FLH1a7j7sM4q53/Te6hrkRePxw8uEIFAAKPRyOO7bZwH+swWjUal+oFzrVwuI5/PyxrE\nxAzaJPZ6PbTbbfT7/ZPaMeleEAw+6soHXUnmnndNgHjb6HMeL7rpdFqCtsViUUr42cjVryEmRYjp\ndOor/FtA73To969eA3im19X9iUQC0Wj0IHg1HA5lsLpUZ9qesoehXqeYMKWz5SuVCkqlkgRostms\nWCNTjKBt3nA4lEDgdDr13Kns3Ph88LXTyUXZbFYqWAqFgryGOrBGD/OvofvdsPpUx3IYy2DCnrau\ncTPcbR68TrSlJs8+2WwWlUoF1WpV+o2s12uMRiNJNGLiEJNAuZ7qXg6cPwy2DgYDdLtduZPqIK4+\nY4VCIfl9evB+4BfU1T1MdPWE3mtdhxZbx84P977oxt2CwaDHdon3j0ql4hmlUkmqIzjc8zzn8ZeG\nrtLRw41x+8WjdUN0Dlr2TyYTORfyfcT3iOsMdKpzxNmJEDyYdLtdNBoNT+mfzr511SEAnsVFZ525\ngob+GuBzs0v+DNebcLfb+WZ3fit6Irk9LFxlTNsx6SCyDqxsNhv5WmZ4Gs+LPjxpYUvPF87Np1ov\naa+4arUqCx0DLjpjkhdTV/jQpXtckFiCdS4NaYzXAyvUstksSqUS6vU6SqUSMpmMp08KN0Ed8GUZ\nId8TxuuEVQ8MnKTTafz++++o1+soFAqIx+MAPlvH/IjKgq8d3vh73L1ZCybA3xklhUIBi8UCABCP\nx5HL5dBsNhGJRLDZbDAej08a0DH+xr0optNpFAoFuajSL79cLiOZTCIQCIgn/nA4RLfbxcPDAx4e\nHtDtdjEajbBYLE7y2vJsqcVbbafI+Wn8XLh+w7qxIRNP6B3Ms94xmM3JIAytmKyp72nRd7pAIOCp\n5KKYmslkpEcCE5rcjNtPnz6h2Wyi3+97Mm5PfZ5ykwVjsRiKxSJKpZKIaFdXV7i6ukKpVJK1mtWy\n0+kUo9EI/X4fo9EI0+lUKjueal9rPA1aZzHRrVaroVQqIZvNyvzUFc/fAvfAZDIpezCFUW3nyT6X\nOsjrVrTqBFTjvNGBXp7jeP7h+lcqlVAul+VxPp9LDG6xWPgmYzAGw+ArAAwGA7RaLcRiMQCQ/64b\nW1OE4Dymi8V4PPbES/ysltzKCNeSiTE6N8nZqnrOD53ExuH2nKMFuh6ZTEZEWY5sNiufY7IIz2p8\n/fUcdJOB9XBFung87tv72C9B38/uNZFIAIDECTnPub7y3qTn8inm6tndgDabjfiJNxoNaXzJ7DG+\nQG5p0n6/920Ior24/B6prrJkxi8Theq9fpG/ZSPWG6YWNrTY4V683YC2tvrRCyQPdPTNM54XviZu\noM3dfJ6SuUHvTO0Ry2ZgbEbNSynnoK4Q0gIVhQetiDIY/L1lgcbPB+dmNptFuVzGxcUFSqWSNKjT\nFjgsUeVmx2DIqS/NxvfBiwRLUUulEn755RcRIRKJxMG6+D2Z3Mc8X/18XnVFBPA5QMKPd7sdCoUC\n9vu9ZGFlMhlEIhHJhrKs8/OAryf3Odp91Wo1XF1d4fr6GuVyGYVCAalUCsFgUIJatGFqNpt4eHiQ\n7NrFYnGySgidIUwPWe7lZqf4c8L1iR7BbtYdB+8quvcX4E1o0pYSzObkpdJEiNOghXCK4xQhuA4w\niMEKCC1CsOKh3+/j06dPaLVaUtXF8/ups2v91mndz6Rer6Ner+Pi4sJzPtB3Vi1C8KzIII2bBWo8\nD6w25D5bLBZRrVbl3sl1yK3SeyyMqzAoxiAwYzu0nxsMBhiNRhgMBhgOhxiNRp7g8Dk1UzW+jBYP\neAZKpVIolUqeJBJWSPFxPB577AVdEcKNdwB/Cw3cJ3kPHY1GsjfSDWK73R7EB2n/RNGLVbOuk4Rf\nw2n3c25isd8wTo+2DeRg3E0n2Lm9HrTgoKsYtaUqk9ndmOCx5uc6cZjVYul0WpIWXEec7XbriWG7\nlRNahGByO9dbLUDMZjPs93tJXGEi+yk4WxGi1+vJIkERQveE0I1jaEukm/3xUatKuq8EN0RuqnqD\n9atC0H6JzF56LG45jRYVeNhyyxy1dRSDL1qEcAPOnJDG88LXj4/cFN3X+CmbDstWdTk+D4Nu9qQ7\nT9xeFK4A4W6uJkIY3wIDaayEuLi4QD6f91RCaG9CncmnN1A7iL1ewuGwiBDX19eS5ci58KMrIdzS\na7/sEWaGEJ0Zwp+hA3jRaFT6TaVSKazXa4zHYzSbTQsGnwnavogZSYVCQXqE/fbbb8jlcnKmCwQC\nnj403W4XzWYTjUZDyuVPKULwksO+TlYJYbh+w8cqIXTWG9cn95zp+loPh0NP1p2d9U4DAwM8q7Mh\ndSaTERHiWCVEv9+Xaq67uzs0m00MBgOphDiH4JYWIdhklr2h3r17h3fv3qFWq0kjz2OVEIPBQCyY\nGKBxA3rG86FFCPrzu5UQ3yNCaKcJ3iPosV4oFMTqptfrodPpyOh2ux7L4+VyefB7bW6cL5wnDI5S\nhLi6usKvv/4qPY+YEJTNZtHr9cTummuia1WuRQgGaQntODudjnwNg7ibzcbTFDgSiWC/3x/46XP9\n8evp4Jeg7Od84e7RxvmgRQhWPdDqVQti7shmsx7BgckDej6xt42+q9LqV/cP4T6nBYZ0Oi0OK+x7\nrK33Wc3N8xz/Br8WBnzv8G+MRqMiQPB58O7M98mp7r9ndwPSIsRut8NisZDDOQ/qsVjM0/h3sVhg\nv997hAb3YM+RTqdFTecBSiujFD44ObgI8nO6u/lj8AtOuwIHqzj00J3S+cjJohU1Ntk0EeJl4ALw\ntYvdUzYe7Z3JjAGdkULV1bUe0Zuy9i907ZjcDdMwHouuhKAdEy/PPMwdq4TQ8854vfAiQRHijz/+\nkMuqtmMCfpwQ4VYO+jXpYja53jN1cgEAOYxls1n5vng8jvF4jFarhVQqZSLEGaBttLRPPrMlr6+v\n8fvvvyOZTHoCVdoTnyLEw8PDQQn0Kf4e147JKiEMZodybuj7jb7vHOsNcqwSYjweSxaoBXFPh66E\n4HrmVwmhbRy0CNHr9dBoNPDx40c0Gg2xY2IlBHD6AKxf406KEL/88gv+4z/+A+Vy2ZMIyL+R9sFf\nqoQwXgZXhKDlIe+dDLa51aaPhZUQjK3s93tkMhlPNvl6vUar1cLDw4Pve4KJdaxqNc4fbZOqE5iu\nrq7wxx9/4PLyUgR3iu7hcBij0Qi9Xk96oPrtf7wH8HOMh41GI3Q6HSQSCdkXdfY5A7IcAA5ial+r\naAAOE03dtfjUa7NxHFeEYGJAsViUXg/VatXT55BrIROAtRuJ28NN23Lp5GBd1cXkTD3vcrmcVEym\n02kA8MT1ZrOZ7P18/vx73EoIvud4f47H45hOp564Oe/BPDuaCPH/w7KQ6XQqWRPaiokboutlRZuF\nL1U+8ACoB0UNXUXB56C9CF3PMC5gX8NvEXM3XwAer21tzQR83vSZjaAzEvTnjZfjR2wybo8Ses6V\ny2XUajVcXl6KCJHJZBCLxQ4y4qiU8kDPBm9sysleEG7TJNskjS/hltO6DRVzuZxkKTHQq70P9fpm\njeVeJ26ppy5TzeVyKBaLyOfzvlndT3md/TKP3DJo7afJr2MgT+/9bmWhn5cxs7C0T60WzSyL+DTQ\nP5rZceVyGcViEYVCAblcDplMBqFQyNMUcDqdotlsotPpYDAYyL53irVHn8t0oDmVSknWH8+dVgnx\nc8JsNR2U1vOCl1wXvzWSwVteaHlRNU4HE4r0/bNarcqoVCool8uSXRkMBmW/Y0VXr9eTNY19Plar\n1dmcoRgIoYhGqwruqayU5XrI+TkcDjEYDNDv99Hr9aRvD6u1TYB4fvTZnq8he5XQbjOfz0sfQp7x\ntRPAsQCtX4NV3RsA+JwAqs9yOlmAP0vHg4bDocetwu4T54vrRJJOp3FxcSHrH62YeEZnwrGuoNdZ\n425PQb/gP+OFDPr69VB17dgDgcBBPI5nfptbbwM3wZtVe1r8KhaLsu5xuA2nU6kUAG+Fvp9FMKtq\ndOUDE4N1f1Y9NynK63NhNpvFeDyW6tbxeIzVaiX3VVZNsLcJexfzruv2JGElNs8QXIsZP+R6fExY\ney7O7gbEKgQ2paFSo0WCcDjsCU6wPNVdYLRgwI/dygjaO3GxZBmMrrLg72egg82BH4PfRq0n3nq9\nRjAYlMY85XLZ4/evhQhOeP7dfg1xjNcBFw9dWcOFsFqtol6v4+rqyuN9zWoXziO+5rSi6Pf7Usra\naDQ8TTn91H3DOIYWx4LBoEfUpWWEGyTxs8qxy8LrRftmMpOJwj2bpnLt8rMLcT/+GvSC/dLQTTl5\neaVAppMN3I/9Grsy60TbELB/AMepfDJ/Vih4svqB1m+0hkgkEgiHw9hsNrLnMZh1e3uLdrvt2fNO\nIUDw3MY9ntlWFPEY2HG9/i2Z5OdBZ8a7WfFfqmp2z/+0tdGJJsbpCYfD8trSjoi9Ei4uLlCr1VCp\nVOT+yWxeejTTpqjf72M4HHqaNp8LtHugwMokOv5NDPK5TTm15U6n00Gv18N4PJaAo4kQz4vrIe42\nTOe+q62YGH9wBVC/IJyfq4MfOr4BwHMWY0BN93piNQbnh90pzgt9jonFYlL1yf3t5uYGFxcXklTJ\n/qvMEt9ut3h4eEC73Ua32z1oWM+zvx8Ux4LBoMQD9RzVohYTN1mprW0LXzoAazwvgUBAqpD1+ZuC\nOR9dwYECLO+7XMdcSy79b37M/Xs0GsnHrECgMKFjt3zUcWYmpmihvtfrYblcolQqyVkgEAhIawFC\nUQ+ArKN8T2azWQCQuDjff3TT0e+Dl7o3na0Iob3u5/O5pwwmFAodlNnv93tP5qNbNsMN0a2UcC2c\n4vE49vu9R8maz+dykeTQ1hNfwg0YU9nXl4hwOIzr62tsNhsJ9uiLiGu74/aGsPLV14duWMk5VSgU\nPI2brq+vkcvlZDFkeaIb4OVBrd/vo9VqodFo4OHhQQ73fiKEYRyDlwO9nnLN1CIES6V1g0Rtn+NW\n3ti8ez0wo4KHolgs5mnGpfszab9gPwHisa/7er0WP1htJaezoxik0ENntjAjk5cfAJKY4KJLX9lz\nIBKJYDKZyOXERIiXhYdlvibcCxkUYVYm97xut4tGo4H7+3s0Gg0RIThPXnrt0aXRrl0JAz3pdFrO\noxTHTID4ueCeyuoYv/4AfvD+oLPsJpOJCKYmQpwHoVAIyWRS9hX67OtRLpelSg+ArGnMfBwMBuj1\nelIFcW4Ben0+YIUszwYUWXlG1FVr7Xbb4/vf7XYlSGNz+Pn5kk1YPp9HqVRCsViUAJxOftM++zoh\nRNv8Mo4C4GilnyvW62DhbreTvZMVEEzC08kkDLDZveL0aAGCr2U2m/VUf11dXYnNVzqdRiwWE4GB\n5/yHhwe0Wi2PCOH2avCD907am7sWrvou4opY2trV7qlvCz0XS6USSqWS9HfwazitRQo36ZxrDXs0\nMP7qNi7XwkG328VgMPDYLum+R1rEWK/XspdSuGNMr9lsotlsyrmPVmS0wuZars8T7lzmmkwxIhaL\nYbFYiIVZNBqViqOXfC+crQjBx+Vy6Vve5wa6gM8Kv+uRpT/WfR04dOZkIpHAbreTQxMHL5G6guIx\nuAIEg8Yci8UC0WgU6/VaBIhKpSLNsIHDhjzum8AqIV4fugyWB3h60lWrVVxeXuLq6koOX1Qu9Vzg\nIsZmTP1+H81mE7e3t7KRj8djj88hv9cwjqEbHekGsa4I4VrLuVlRbhayzbvXBV97vuZahNBVhBT6\ndSD1Ka85Ew7oE007OYoSFFTdiy8vrzqzhRaNkUhEymhdGAikDV61WpXzA5+L8bK4lRC1Wg31ev1o\nJUSn08Ht7S3ev3+PXq+HXq8nIsQp1h63SZz2ndWBOp5LdSWE8fOgRQheOJl5/JhKCGaPcm20Sojz\ngpUQ+XxeetlQjNCe0zqZjiLEZDLxVEIMBgOPpci5wPVNV3m5lRDMHuW+PhwO0el0DoQIZoOaBeLz\no5OMuD/R/oMiRLlcPrh38r5Jp4rlcnlQ5aKDvDoj99jz0L3DmHygk544X1zLTwoQxvmgLb5YCVGt\nVnFzc+NZ/3iWC4VCsndRcKUI0el0pApMN/D9mgjhihFuRrcOrmoxwhLl3iYUITgXLy8vUalUPBWK\n+XxeKvt1Qrq29NX9FZgEwgpUHctdLpciGjw8PKDZbKLdbnvWSIq3frFhnZRSLpdFhLi7u8Pd3Z30\nhGL8kFX+TLJzRQi+X/iepNMP8LcoMRqN0G63JclZf+9Lra9nJ0Ls958bNz8HfpUSDKpRYNjtdh4P\nL4oQ7kHrsX+PO9FciwlO+Ewmg0qlIoEWnS0AfFZsmaFJddj1UTTOH23VwIWHdkzM/ry6upILqdug\nSc8FtxLi7u4OnU5HSsFYCWEYj0VfUpjtpi1uUqmUrFEMjPhVQdi8e73oTEcG9/U+yQoJXQkBPE2A\nAD5XQrAxXbvdlh43HMwC0RkkbJiuhxYg2HfJRdsxFQoFVCqVg+pL42Xxq4So1WoeuxpaMrAS4u7u\nDv/61788nqun3PP8RFztm86mc3Ze+3k5VgnxNTsm3aiQvQMeY1dhvCxahGAgjgE47TnNNYt3Or9K\niPF47MnWPRf8KiF0cgKD11qEcCsg+LHxcrASQicY+VVCaKFcW5Ewo5e+5roHHOcn17eviRD6kT0P\ndWVrp9NBLpfzVEKwUsJEiPPB7SGoA783Nzf47bffUCgUPFY3jItRhHh4ePCthNBr35fmEz/3pT1Q\nCxPG20fPxUqlguvra9Trdekxx8EYLBPq/O5+FMh1EghFNN3D5P7+Hre3t7i9vcWnT5/w8PBwEAP2\nO/sHAgHptVitVjEej+UefH9/j48fP2I8HkuiFu0eY7EYgM9VZxQStBUTAKno4EgkEuh0Ori/vxcR\nQou8L/Ue+elu2Xxh9ELFaguqo6yEYHkoD4jMWuNEfAxahHCDdGxexsCO9th2n896vT44oHKRZsMy\nW1hfD67dCQ/vnAPsC+IKEBTpKGDN53P0ej3JmBoOh+KjyOw4u5ga34LuV8J5mUqlPD7mgPdA5waG\nLavkdeHnZa9LWNmr5urqCpVKRaxxuFZxb/R7zd2MJG0rqBsSdjodKTttNptotVpy0NOVEPpSwspA\n/fFqtUIikZBLNUtl3YuSDhRR3NA2Obyoa+9OW09/LPr/M+0J9eWApdMUH9hTi3sfK1V1VhKz4E6B\n7k+STCbl/ZPJZKQBreutvd1u5TLDUm196bZ19O2hm/rqAC6zfV1rOwBSeciEk3a7jVarhVarhcFg\ngNlsdlaZ8gY8QQD+m7i2Idy79Dnq1BYhfL48D3J/jEajYrNSq9Xkkc22E4mE+LNPp1P0ej3c39/j\n7u4O9/f3Yptndocvj59NoFvVqt0mmGA0m80wGAw8g3uVToTUWcZaQNBzx21arR95v4hGo8hkMiiX\ny7i8vMR0OkU0GvWcCSeTiSfr3fbKl0ELSLq3JcVHNqGuVCooFovS2JdCE89xs9kMo9EI3W5XzvyD\nwQCTyeRklprG64Pz0e1Hk0gkUK/XpQ8Tm6Jns1lP0oe2E9YVX/pxsVhInI2PTHjSg8K6rsh+rC21\ndvHhvZr/BiBOOhQoEokEttst8vm8JN4xtsjfw39zbefPdM8mp+KnEyEAb5kKADkI6YCGbhzN8hct\nDCyXy2/6fXroLDnaQejMUn0I0AdRLULQd8xEiNeJXhi0EOUeBP0WCZbDshTfT4TgJk6PN8N4LNwA\naSNCmxu9YftV5HCttODZ60P7BFMczWazKJfLqNfruLy8xMXFBSqVihzi4vH4V9cqogOv3F91Ceti\nsUC73cbDw4P0tGm1WgdZJtzndABHZ+PxZ1GAoJf2ZrM5uPhqEWK328llSgvAoVBIAt3z+fzg7GBz\n/PvQ1gvcB7UAQZsanov2+728xvRHZfWDtiw51VkoGo2KxQ4zASlCxGIxOdcx4MjBilu3uvXUQUjj\neXCb+uo7ANcfoivLtAjRbDZxd3eHVquFfr8vGfXG+eC+d90qQS3Ia2tL973/0oE41+Od65oergjh\nng1CoZCsz+zd8/79e2k8Ox6PTYQ4Ae753q1qpbWmbri62Ww8/uR81Od97rnct5n1ns1mPT27dFUD\n4yB8XvQ355yjCDGbzcR6k1U0ugmxPhPaXvm8uMk8rDjWjX8vLy9FlKQPP+1geAaiCEHLLTal1iKE\ne/6x19bQHNunuM5kMhlcXl6KEEERQtvqM+mMP4vrnu67xcoHrj3s+cB7h+75wCRx1wr9sQKETsrS\nd2sm+VGE4BrItTccDiOVSnmS7Pgz+fnH3NVfmp9OhHAzMjkpdK8FbQmlmz7zv6/Xa0+DpMf8Tr2I\n0tqEmX/a4klXQuiGJZvNxleE4OXVgs2vD7cSQlfDcAFxS1Y5B7koch5QhBiNRhiPx5hOp5a5azwJ\nbRXGMm29PvHS4FZBuJUQxuvBbUZOcZxZaL/++isuLi4ky02LELxM8nDjl8HrzhMGJ/RoNpueJsOt\nVsvTP4lJAW5QhtnjOkOetgJahNA9lnjZTSaT2G63CIfDciB1q8+Gw6EnC59VkDbHvx/XfoH++By5\nXA6ZTMbTC8xtzMtxDr7ivJDT05VNtd1KCJ41+XdMJhOZq1YJ8fbRIgT3WJ2Eos97fOT6o603b29v\n0el0rBLizDlWIegmceg17NQipM5QZ58eBpbZQ0mLEOVyGZlMBrlcTta69XotiVL39/f48OGDBBlN\nhDgNfpUQFCHcrGDd/4GWOZ8+fZLB9UYnWOr9m1nwWpSgwBqJROT5MAjI+cZ5zzPodrtFKBSSKsNg\nMIjVaoXxeCxVribYvwxu1TQDv3ydC4WCZJ6Xy2UUi0XkcjlP81ye49h4nJUQDOxOp1NPwhFg523D\nH85mk5IAACAASURBVH1X03Mxn8+jWCx6BAgmBemqHZ633Ip9WghyDAYDtNttz5hMJp7kAVo1MS5L\nEQI4TEg49rdoIcLvbk0RIhQKSaxFWw/r9yaTC+m8c4596H5KEYKP+hCoywEBeC6AbsnsYrE46tn6\ntd8LfPY9ZuDjWCUEA84MrviJEAzOWCXE60JXQjDYq0UIvfC4cA5OJhPxjXUrIWazmfnyG09Cl9jS\n1iadTvvaMem1UV+cLXj2unB9guPxuFRCUISo1+tyCdTNu/Sh59hrrvdPvX7pfg8PDw8eywZm2h1L\nCOBjNBqVPTIej2M6naJUKsk6SBFC/628iCeTSREj8vm8JwDIn6/Lx6fTqaeZon4uxrej+yJRgGDg\nQmdSausivtbakmk6nXr2u1PaMVGEqFar4j/Lag6KECzv1n0saMfEhJLHZlAZrw8/OyZaovhVQnCw\nEmIwGKDZbOL29lYa/poIcd64d0/gsJL0WBXUS68BOrATDAY96xoba2sbpmq1imKxKBVtFCGYPNfr\n9aQSgklz/HuNl+UxdkwM6nOvnc1mUn318eNH/POf/8Q///lPuVvqgFexWJQxGAxQLpcxn8/FUYKW\nPPw+JjXp+AvPk5lMRhJIeEcOBAIiQHQ6HY8F4zkF194y2jaGtr35fF7Whnq97rFjyuVynnXMrYSg\nCNHv9z1JRzqAaxguriCm+8npuajtmPL5vKfqn0F6HbvQfYx0/yJtF9xsNjGZTHythXUywWP3bv2e\n0iKErt7XIgSrNQDI3rxarWQdZgxZ2zK54xz46UQI4OmNM783o5wveiqVElWfF2+WLPISwt/HpsNc\nrBlk5tCT34LN54u2AgkGg5L9lslkpJmq6wus1VkOLo7j8RiDwUAWx16vh+FwKFUQ32IXZhgabmIs\n12YlBDPftVDLxugMBrKPjlVDvC7cDAxtK0NLpnq97ikT1cEyN3Dvrlm00GHgeDweo9/vy2CWXaPR\nkANeu90+aHLuN6cYzODlhY3udFXYbDYT31ptPcWLt86A8etrwnU3Go1KXwoLDn8/ui9WNpv1CBB6\nT1wsFpKQwTMRX1f27zoVulpRZwzzElQsFpFOp0WE4LluNptJUgktFHUfJxMg3g6ubQAFCO6vfmc/\nogM36/Va7gK9Xg/NZlMqgfgeMc4XP2smjZuclEgkfKv3f6RA4c5NvT/yTMCGmZVKRc4ClUpF1rhK\npYJcLucJotC/mslStFyx6ofTwteHc0xnBOv+Xjy3TadTCca1Wi00Gg18+vQJHz588DSJprihq1vp\nma57YGYyGY93uStk6OeZSCQ83xsIBDx+6LyPUNw/l8DaW0W/zrwrJBIJObtVq1Wxbi2Xy8jn87K3\n6YRZVtbw/E97m+Fw6Ek4sbOPcQy9V3FN4/pSLBZRq9VEgKBVIO8VLoyhamulfr8vax5Hs9nEw8OD\n3FGn06lHvPjWGKxer/THFEV0QihFWK6d2+1W7tO0oaUQ6ybJ+50d/BIe+LUvyU8pQrw0ujQmGAxK\nuVC1WsXFxYUs2vl8XjIzOcFGo5EEaej9ygadfhPIOE+05VIymUQ2m8XFxYUMqrSFQkE8M7nQ0GqE\npV5cDGld8vDwIB6rtBMzjKeis+J5Edaesbyk6IBIt9v1eP2y0a/xOvDLknCzMvwyM/zQGSG8yDJb\nl+K5K6aPRiP0ej30ej2Pl6bb1MsPXdHIAIfb8I7BYdr+aG9MXmCDwaDYUJVKJbEX4IGNc30+n8vv\n499rPA1tx8RAVzab9QRkWYXCoD39UFnpcqpsWp2BxfcJkwv4t/DvoZUdgzvMsup0Ouh0OtKQkTYE\nJkC8HXiR1B7orC7koI+x7kkDeAUI3TCRlmRMOOGF0ubL60HbXqZSKVnn3MDtbDbzzAF+nR5PXQP1\n3OSj29wzEol47JdY/UCxmPcVbTXH56qbd1rVw/ngnvNcAYBCOS1/mew2HA7F6pe4VT3cq7l3U0zg\nvjifz6VnSDgc9sQvXPGVj3r+uy4Vep+09e95oYBO8SoWi6FYLKJUKqFSqUjgt1gsIpPJIBqNYr/f\nSyxLn/+bzSb++usvtFotaVJ/6kpW4/VAS0s2u49EImLBVC6XJb5aKpXEPti1kubawfsi7xe8Y7DJ\ntO4FwapT7nNPOau7dwfeH1jdwLvrYrFAJBJBsVjEbrdDoVA4+JtLpZLEjxOJxNF7OUUWJu2xp/B0\nOvWNJ7/Ue9BEiGfGT61LJpNyqLu+vsb19TUqlQoKhQKSySTC4bAEnHlRZeCZQZrFYmHWJ6+IeDwu\nCySHziJi2WI2m0U6nfYELFgOO5vNMJlM0Gq1PNYlFKdGo5FcXgzjqbjZeCyvdwMkOjDb6XTQaDQk\nOMj1yXiduBmRuvLha56S2vOemXSdTgftdlseeZllthytBnkQdC8kX9rfWL2gK8e012w2m5VACht0\nsScT/1aKDSzn5dfFYjFP2TiDydoL2fbfp+OKELoqVF8atA0Ns+ZGo5FUDpwCt3qI8yqdTiObzYq1\nFEUVZjBRhOC6eX9/Lw0ZebmxwMrbQdufcGQymQMhghnJflWwriUsK8s4XyhC2NnvdcG+gAzWsnqA\nHvjxeFxEbz0HaEGnG9o/BVpYcF7q4CITT3h3KZVKnqEFNFZ5USDj3s99nkEOW8vOBz9rDj7qKhb2\nn9F7lBbKNdpmh8kbtFXK5XIyF5bLpayJes1yLZWOCRDu2dAE+5eBoiWrVxnL0kHfer0u1Q8UIVj5\n0G63PRnld3d3aDabGI1Gnl5Y9noaX0NXlHIwxkZBjH0M2ZONNnB6L91ut9Jni0lBnU7HU6nPRHAK\nFLS+fEr8VYu/ri2UFiFoNce7azKZxGazEREiHA4jEokgl8tJ/JjVYn7QBpnV4xRUdCLLKWLKJkI8\nM1rpYqaJroS4vr7Gzc0NcrmcXFZDoZCU2eiLaqvV8ogQ2m/MAiHnTTweRy6XQ61Wk8oXZhCUSiVP\nxiQDXQCkImYymUhzHF0JcXd3h3a7LRcSq4QwfgSuJYCfCKGDae1221ORYyLE68SvEkLbL+kMjmMw\nG45Ndzk/7u7upN9Dv9/32DO5HzMQA3w9CMv9Tx8KKUL0ej2pfNAChLYA4CNFiEwmI1+XSqXkZ3U6\nHc/aTPHDeDpuTwjug641JQVPXmS1CHEqCxpdMcaLga6EoAhBz21XhOBFg03Y3UoIwASIt4AWIXi+\nYwBXixHuOgt4RU5dCaEtEM2+4nWiKyHora6r8mgtoRPO+DrTmpd94KbT6ZOfA9cnDl2xrSu3dXPh\nfD7vESxisZj0EWDwmqIxKyHMKuw88Kt4dSsh/ESITqcje5RfJQS/j/sXg177/V72Q937iMkcrnCq\nhQg/IdbPmsx4GXTgl8kWzDzXlRA8uzHou1gspO/bp0+f8Ndff8l9kRnZ2sbXRAjja/BcxZ6q+g5R\nLpdFhOAelkgkpL8f726s1mfPGwpjd3d3UrHDwX2M91MG7YHHNZ0GvPdNnbzEuIp2wOl2u557hRYf\nOPg52rofq4TgekwrZDoPfKkS4qUwEeKZcRUvThotQrx7986TcezaMbXbbREh+v3+waHOFuvzhypt\nvV7Hr7/+ipubG8+BnqVUujRaV0JoX1VWQlCE6Ha7njIrO5QZ34OfL/GxSgheNLUIweCgBWhfF643\ntN63eOjRX3cM3TyYzadbrRZub2/x/v17vH//Hv1+32Mzp5vx6myMx6AvqFwztU0YfYPZiDqbzfr6\nB9OOiV+33W6Rz+dFgNC2Ovydrvem8W34VULkcjlPJQQDG1xrWq2W+Aefgx0TLwO6hw4vQ6VSSUqn\ndXWjriC7v79Ht9v1BHjsTPd20MFmXpa1AMFHNyCohSgdgGamOUUIywZ+vfA8xYxwNu2Nx+MS4OPe\nyCDBdruVbE1+PffmbyUYDB5U5LjzUveuy2QyMnSPu1AoJAFlVj8OBoMDEcLm53ngJ0C4IgTPb+w9\noytYtVAOfA7CBQIBCdAtFgsR1YrFonwvRYhYLOYJevlZMfG5fGnY+vdyUITQ55xSqeSphLi8vBQr\naa5brIRoNpv48OED/vd//xf39/eyh7n2NobxNWjHpHvK+VVC6OC9roTQfSCm0yl6vR4ajQb++usv\n/Pvf/8ZkMsF4PJZHbcP7lB4Q+n6tE/x4P9CVEMPhENFoVCogisWiVIm74oW+X/CO4YfuJ8s+LG6l\nIkVAq4R4Q/CNokWGXC7nCT7ncjmP5zbw2ZORFjwsBbLS1teBG9CjzYduWKkbUtM/UX+fG3zpdDpy\nGOx2u+j3+9KMWpeX2bwwvgX3QsKGmalUSsqoM5mMiGRaVWfTOjZJp7LOi7PxunEz5lzcC6D212Tj\nOQpUzWbTU9bPQw+zUb4HN3Oce+dwOBS7CR5UC4UCFouFZG/yQAhARBf+HAoSFB8YNHIv77bmfhvc\n53TfGWbWUYBg/xkGXvU+eA52TAwe6gxi7ukM3iWTSc/84rmOlwGum5Yx/HbRGXu8MKdSKY+4z+oq\nDSsfdEPPyWQi9jtsRmicH/q1Yy8bZmQySMsAhq4spHWNzjhmxqUO6umgQzwex2g0etLzpAhBYezY\noPWKfnT3fQZQGOSg/aKu8DJOj1uZxbXItYID/O2QvoRfdQLXKt5NtXjlih/uz9IWdLpprGvdY8Hr\nl8EV1F1xkmsFRSxW7s3nc0lI6vV6sjboRCSLXRjfgk6UYyCeZyyex1OplEco1zaXWlDQTakpjOk4\nK+O4fhZ2X4LiqtsDguuvtkLMZDISD6YtvxZYisUi8vm8x75J30dp+c/fqx+1owqTRtvt9kFS+1N7\nXHwPJkI8M7pjOw90bJSiLQf05OFBkwu0XwmQZbufL35NZ/TiyAN/IpGQZr/8er7x+frqQ32r1cL9\n/f3Bwf4pqqxhEFdZZzBQN3lipQ6DgvQknkwmYhWmS/usIufnQGfoct9ioJj+r61WS/qFsOn0c5d+\n6gDQaDRCLBZDPp8XT8/pdHpQ2vrUbFLj8biHeF4eeGngvqgDIrpJpmvHdMpKCJ7tuF66dlLMbgLg\n2Z/py6r7oOh108TbtwUDN27Qzz37u+isco5zmPfG1+Glfzweo9frydlJ2yesVitPQFZnR8ZiMVkv\ntM0g91sKoLT2nc1mT3qegUDAY8X0paGFYbdPyW63k7tKr9eTPb/RaIiFsFnFngecX7pij1bQtATj\nPGQQLJfLSWBuOp0eiBVfQq9/8XhcxDh99z0GBQj+Xl1JQWHDLHxellAoJK8lEy302sAESp7BtQ89\nXzvGsnTg0zC+Fz8bt8fY+fqd0aLRKJLJpEckc/s4HLM+0s8BgCSYa/tWvocoRrgifyqVQqFQQKFQ\nkCRQrs8UHI4Juq5dne7f2e12pSeL3ptP9T60W/czQ2/pTCYjE6pUKiGfzx80X+SmrpV/rc5pEcIW\n7fPFr6krsz11Ob4rQhBdZqovMs1mE/f391JGxYuoHcKM70FbL1GR157m5XIZhUJBgiauCEEBgpnJ\nJkL8POz3e8ki4Z7FQPH9/T3u7+/x8PAg9hFahHjODDYGrqfTqczv4XCI0WgkftWc7/oQajwfbq8R\nihA6Q1zviwx06GAsxfhutysX21NlgzNIw7Od7u3EIEsoFDpogucKuBTFGFixdfNt8SUR4ktBOO3L\nTu//c+iFYnwdV4TgPYDrRT6fx3q9ljsC8Hl95H2BH7sWCbRtYmX1bDbDcrl80vMMBAKeqgqdmekO\n2lr4Jcy5Vg/NZhN3d3ey95sIcT5QPKeAVSqVxAKRe5br/c+m0pPJRL7msWh7Vy1CuIFrP7S1J/dN\nHQtxs3eN54Wvpd7LdCWNTrrgvYAV87r3G2Nbp2iEa7wtvlSZ4FrGuZ/TNnJuVbbeb3km1/3fjiWQ\n+Akg/B4OChC8+7CvEvdZXR3BwfeYe4fyqyhzKz1Wq5XHAvbh4cFjn82+U24FxUtgt+5nRldCFItF\nVKtV30oI95CpKyGoJrsbr3G+UK3UftE8gHHj5sJDlZQBFx200BcZZhfp7Ekd7LWDmPGt6EMl7eL8\nKiFoEcDM5GOVELo3ic3Ft4++bPCCwUqI+/t7fPz4Eff397JmMRjx3MKproQA/j48UoSgz2c8Hvcc\nQF1fYuPHow/Q2qpSC/Su9RUP0bovUq/X85TynwL2s0in0ygUCqhWq76NtSks6MpWtxJCe7JaJcTb\ngmdB7rF+c9wPLaSekw2Z8XX02Z17ixYs+fpx79PBBIqvnDPuPrnf75FKpTyJak8VpHSDTP3ofuwO\n4HMVJM977l3l/v4ezWZT9ly3j4BxGiiGfa0SQvut53I5jMdjz772WNw7Bu0tv1YJphMydUWYm5Cp\nA9g2v54Xt6qFsQwtKAGH9wL2fWAcS1vSufZdhvEU3Lvb1wQI/bErrqXT6YNAP8/7Wqz3E2O1KMp5\nrb+Pg/EW3QvYtfnlWsnhWra7AoyuhGAckbFkugJ0u10RIbg3LxYLTxzxJTER4plxs+V4UdWZB262\ni7Zj4oJN9dgqIc4f16tOK5+0nEin0yJQuAcxnWFEHzdmgDYajYOMAtvAje9BV0LoPhBahEgkEp5L\nL5tSM5OXIsRTmzYZrxP3sqEblTcaDXz8+BF3d3ceIZ0ixHPCAB7wtyARDAYPKiHoF8rDp/H8+FUJ\n6r2RZyJtx+RmhLNB5qmr/3SCSaFQQKVS8a2EcO0J6Dmr10/OVTvXvT1+VCXEudiQGV9HezDv93us\nViskk0kUCgVMJhPM53PpkaD7LXGuaAGCuJmO32I58SX8ghhuQMfvv7t+/a517N3dHdrtNhaLhQSO\njdPDYBrFBT8Rwq2EYK8j7mvfWjWq7xishHD7YPrxtUoIs6Z+WY5VQtBWhnPH77yj7Zh4jzSM78Fv\n3/LbJ/34WiUE43a6MoGCAe0J/dYuvx46+nuOPQKQuB+Tkdzn8C3rrl/zbVbTUoTge9KthHhJTIT4\nwbheXSyZLZVKqNVqqNfrKJfLyOVynkavXKg5Go2GlMvQekc3eLKN93yJRCIHjd2urq5QqVTkdWfW\ngFY/eeBiwI5ZxWw+zeCZzn6yoIXxPehDJQUyNlbVGUuRSEQqsNhojEFlihKnamxkPA/6MHesQmC9\nXmM0GqHX66HX66Hb7Ur1Q6fTkb1Lz5GXmBduQ0VXHDtmBUVLRP2ovbfL5bIEl8bjsQSYjK/jZu7o\nLFzdWC4QCMhBfLlcYjgcSuBOBx9OjW4wRxGFl3GdWOD6I7t2EpZU8rZx5wl9f3UmsJvJS5s7Zpez\n0a/ZMb0O9OvHvYSWqvF4HMFgEKvV6sAGifcC7T2tk5WY1KT3Lw7aIjHw8Jg1RSc8MfARCAQ8GZqx\nWOxAKNNe0ww0MlOd9xQGjHlXsXXuPODrxsD+eDwWcYDzxq1e4D2WdwLOjd1u55sxrOcuYx/FYhHp\ndFrEed0HkfPOzex1m7MyyYVraDwe99w7zNbneXHFKTbS1dUQwOc9j0Fdvn5MbKtUKgDg6SfnntHt\ntTS+hGv5FQ6HxZmBg73/ODQ6UTiRSCCbzUpSr95r9dCVDMds6fzWIvf7tAUTBwDP/r3dbj39Cr+U\nrOK+jyguMK48Ho/x119/odFooNPpSCKL7q1zqveaiRA/EC68egIz06BcLqNareLy8lIWbooQtDdh\nIKfX66HRaOD29hatVguDwUBECLNjOn/o5VYul2Xc3NygWq16Xnd9eAMgthPa5ob9H8bjsSwavGzY\nJm38CJgZxd41mUzGYxmhszV1ZpIWIPSh0QSIt8WXLIpWqxWGwyEeHh5we3uLu7s7aUbd6XQwmUw8\nQYiXXrOOZXT6fez3tYFAQCx3isUiLi4usN1u0ev1AEAqgozH4/aGcANtzK7lPtfv9zEejw8sTE6N\nFlF0kznX59q1YtJVrXaOe/u4lRDH7Ei0cLrf76WE3k+EsEqI80YHSBho7ff7kvG4XC4xGAx8gxJu\nLwbto88Mcldkp+DBwXvC12DfOV2pGAqFpB9YoVDw2Kxo9FlQiw/MembVI9fxc1m3f3Y4V5gZ2+v1\nPE1YmYHr10yawX+dvasDZfwePZdLpRIuLy9RKpWQyWTExkTvj/v93iNkAJ8D3gwQ8lwwHo8xHA5F\nFHHFt3NIUHircC9jtjhjWel0WvqbutUSXKuKxaLYSM9mM7Gq5D1SJ2bo19Qw/OAZabFYIBQKYb/f\niyODdmfg3qrFVa4znK/JZBK5XE6SztLptJzttVWhrkxwq1h1AokbB9Hfoz927wr657lNp4/BuKF+\nH41GIwyHQxmDwUDu57yXawvYU95DTIT4gXCC64NlNptFoVBAuVyWSgiWsPFAudvtRIS4v79Ho9GQ\nhp5ahGAwxw50500kEkE2m0WlUsHV1RWur69Rr9dRq9WQz+eRSCQ8FTPaa5/el1w4+v2+WIjwYK8V\nT5sHxvegAySshNAihJutyYsAL7tuNq8JEG8Xv4MQRYhGo4H379/jH//4hxz+hsOhVG7pLKdT8K3i\ng/bqjsVi0tOJIgQAafZ1LEPFOMTNdnRFCFZcMbtpPp/LHniOIoTOcmfFmO7xpDONmTFMEcIu2T8H\nblaxtq9wRQidBepa3LTbbfR6PesJ8QpgZQLXgM1mg0gkgkAgIEGCZrPpySrnx64PdDabRS6XAwAJ\nymrrXooBs9nMkwX6mPmx2+08WaOTyQSRSASXl5fynLPZ7EEWKb9XZ9TzZ7h2sTrL2Tg9bq8ZCgzZ\nbBbL5RK73c63mbQWIDjYO0KLZvprk8kkSqUS6vX6gQjh1+uEa6Fujp1MJqXiYrvdinCSTqeRSCSw\nXC4RDAZF1Lc99fnQlRCZTAb5fB75fF72NNrFaDs5xjiKxaLEMRaLBcLhsMf9Q2dm67OTYfihK/GA\nv9c13j0pRIzHY4lNaAEC+LzGcC0C4Kl61zE6fuz2TNI9GPSj+7Hbc8ntt6RFCN6NdJXZMSGC7xHd\nAH42m6Hb7aLdbqPT6cgje4p1Oh1Pb8ZTJAdqTIT4gbgBPaprFCGq1Srq9bpnww6FQpLxRBHi/fv3\naDQa6Ha76PV6IkK4ZYfGecJKiEqlgpubG/zxxx8oFosoFovI5/OejAHXY1V3se92u+j3+2JHwU3a\nMs6NH4lbCcHGqtqKifPMrxLCVdNtTr4djnlEE2Z0Pjw84P379/if//kfT0YGs5tc+4iXxO9v+FLl\ng7ZjCgaDnkoIBlVYATEYDEyE+EZcAcIVIRhg5T74WiohdC8Lt8LRrxLCqhl/DvzsmPx6Qujsdq4x\nrgjR7XYl09zsmM4XBgcoQDCJgwJEq9WSOyIDuvx3JpMR+5tMJoPlcon9fi8ZyPz5bv9ANp7s9/sY\nDAaP6sGw2+0wGAw8IxaLYbPZIBqNIpfLHb1ruk2D3UoInhHNJue80L0Gh8OhJEsyYUSLEFyzttut\nR1igYEa7JCZdUjTjPSKbzUryRqlUQjqdlt4BWnTV50LXCjOZTCIYDCIajWK326Hb7SKXyyGTySCZ\nTHosz2xNfF50dYquhODr71ZCUKSKRCKeXjjr9RqRSEQCxhRoXQtLwziGrgBkXEKLEBxagND9bHRw\nn/M1lUpJoreffayu1tLnNv3o4lZ++32shRHeO/m8XYs6wt+n44asAGm1Wri/v8fd3R3u7+/RbDbF\n0p1DxxKtEuKN4NfchJUQ9MGr1WrytZxUm80G0+kUvV4PDw8P+PDhAxqNhvhO8wJuh7jzRQe6YrGY\npxLi999/RyqVQjqdlguoLm/Wnq48HA4GA3Q6HbFjcpvZGcaP4kuVEAyqUWRgRi/t4bRHu61Prxc/\nixydlXEMlse3Wi18/PgR//jHP17wWR/HPTj6jWMHPH0J1tl49AxdLBYYDodIJpNyeTIeh94nOXRG\nEA/h2opG+5ee08XUrYLQgWW/6jFWQuieEJZM8nbRF0u/jGI/EUL3sGGW+Xg8lvPgYDCQnmBWCXG+\n8H2vM3lXqxUmk4knG5LnLAZ30+k0crmcZ6zXawnCJpNJWQdZhUqxir2ZmO24WCy++jy32y263a5n\nMMBcKBRQr9d9z3a8r/AsOJlMpGLbbR5snBduxX0kEkGhUBC3Bc5ZvW7t93u5w/KOwAoZXRnBLOJC\noSCPeqRSKQkC8j7BwUQEHTQMhULSa4AVQIVCQUSIdDqN+Xwuf9eXmlwb34/bEyKXyyGbzXrsuADI\nekXruGg0ikKh4Dn38Fyt90G/XlrHssv5fI597th/+9Lf5vd9dq89T3hP4FkpGAzKfYFOIhTRuabo\n+al7slKgALyx2a/NA9eCid+vf86xJLin/s3uYBUE44b9fl/skf/66y/p0bher2VQbD4HTIT4gVD1\nZbYTF2jdhI62O7qUlkIDB7PedTmrcZ5wYdODrzsPSW5AV4tP2lppMBhIGdXDw4P4q/f7ffMANp4F\nBmlZUs3LBA+GPNTrkn9mZurgoK1RrxtmtLEpOS+RupHgMc7xkM5DJQN+zNji33csGxnwHix3ux2W\nyyXG47EkCTw8PKDX60lJ67kc5l4DbrY3rZfYsFlXOzChQzd85h7qZ+fw3LiilU42Ye8vZmcyCxD4\nvHbyojCdTsXKztbNt4fbmDWTySCXy0lQrlQqedZWXowBb/Ca7w8Gmik8WJPf1wnXKr7nac2kK6Zc\neyXeB8fjMTqdDhqNBkql0oEHPisStQf0YxKWdrudWFcwE57+2W4Ahs+PjxQ9ms0mHh4e0Gg00Gq1\nMBwOMZ/PbW07U9z9KBqNHghHbjUEAORyOVSrVazXa4RCIbGTdhuusq8c78A8a9EyaTKZSPNU2vDM\n53PZS3k+SyaTnj2e9xQmhDCxk17wgUDAY4FmPA+ujSbPYzqjWyfyAH/Hxjg3VqsV9vs9YrGYnJt0\n7Etb6YzH44PGu/y5OpNcV1u5za35/cfmhLbd0Ykj7u88h6xx4zO6Yp2vy2KxkMr8UCiE1WrlEU//\nP/bOM7mxLMnSDq21oAhRWV3dbb2TXsSsb2YPs5aytsrMCBKEBh60nh8xx9Pf5QODCkGI85ldDCfl\n9wAAIABJREFUAyKCAQpcXuHH/Tj6yGCtQhUXYh3WJskN9rsNp+08sHPLChvuwN+/RYTAORCPcE2B\n5VKn03lk5W/Pjcc2fylCvCOuSoxNGJdoBKBRPoPhepjhosos4+PHCk84iCHgBRECi561asBGB+sS\neKv3ej2fCNHtdmUwGMhkMqEIQQ4CNl9bUm03ZrtmoeQfFTpYp44pQ5m8HFg95HI5KZVKUqvVpFQq\naaBsn+XQse5LNoCdz+elWCz6hOGnmsO6j8hghQgBq0SIEMf6MzhG3GxviBCoEkA25m630yxIZOa6\nPRdcO4dD41bS2Oox2G4ioxnCHTKGrWcrRAgkmHD+nA/WkhXCPjz9i8WilMtlqVQqanloBd6g3w1X\ngMBlkvY2p0dQACvIsg3VBVj7BoOB9Ho9X2DXFQRgD2jHc+4Lu52/MTUC0K5FDtYxW33heZ50u11p\ntVpq+9DpdChCHDnW6g1nf2v3i/VFRHyZw/l8XjOO0+m0lEolrZawDVdxtrJnLATgME9ns5nPAmw4\nHEqhUJBKpaIDdw9bzYqKCJxT6/W6Cibr9VqrIshhCOrlhfOz61tvn+Msl8vlNMEEVSwQoSC62jkx\nGAx82duIhwVVz9qkTlthY4cLqi8wYrGYinTu57RiBvfe48BaEoVCIZnNZjIcDiUajcp6vZbRaPRo\nPcpkMnoPxKNNcsLYJ2YFCWIW3FFs82k73mLfi/uo7aPS7/el2WxKq9XSgapGK0IcQ/+HIChCvCNY\n0DDRkXmJy4YVIXAIwC+Nq/5i4rAS4rixwhMajrsBL5SgYnGy/pW2ASdEiFarpSKELXGmCEEOAQ73\nyOwNEiEgmFnfYc/zVITgGnXaYN/K5/NSqVR8IkQymVTh9FSwlRAQIWyFGrLzsC679nj2OSxRut2u\nihCDwYCVEC/EzSpC4MCKELYSwvaqcUUIWMTZ1z0kQX0sbB8dZPS5/b5wMXYrIWCtyHXzvHC91O38\nsCIE7gS2EsL+brh+/7gLoIweg5wWNpPSvt/ILkeTZxu0QMamFWTdrEy7jmI8Z37syyB27SVgjQJb\nucViofeVZrMpd3d3el+BCMH5eZxgP0IwLBwOq91vUCUE7gf4czqdlnK5LLe3t4/sFF0BFhWB2Adh\nMTccDn1Bs1arJfV6XabTqWw2GxUb3CC3WwmBj0dlBSoiTumsemrY9xzvzz5bU/uYSCQkl8tJNBpV\nWzmbjGsTMe1AHy0Ma92Fzw9rHtdyxh0u2KvtgC32fD7X+y9EDLtekuPACuWohFiv1zIej6XT6fgS\nhNHfDxb5eERjdeyvqVTKJza41nEYWCft3Ic9qztQzbXdbl9tG4dk0MlkonstkpYbjYY0Gg15eHjw\nxZMRS3b392OBIsQ7EuSXh0oIXKAR0IMFgS0/Q8AZF1WrvJHjxGZnWD9XN+vWLflz/fXR5NSthMAC\nwmxzcggQWMPlwYoQNkPcWgTYSgjaMZ0HthICZe7FYlEymYzPWsbFLVk9FoIqIWx1mrUICGoyhuf7\n7JhsRjuDLc/HChAi4gtsIQCHj3ErIfbZMf2qnhy2EbWdX9aOye2pYkUInPfcSghyPthKCCQj2UoI\n2DFB2EUwR+RxJcS+agh87DGtt+TnuOuVrWKwPYrciqsgWwf7enhus3VfEmhw7WT3VULYdcz2oGg2\nm9oEEw2p0SyUHB8IZImIvkfWjgnBNbd/Fir/yuWyVsTsa95qx2az0axdt//l9+/fdXiepwIEhNtE\nIuELemNdhQiBNRHzMRaLfeSP9iKw65LtAxGEa8cEEQlzzF2z0Puo3W5Lp9ORXC6n8RE8oqm1Fbsw\npyGQYp2yIyjwCxtaO3DPtS4Ads1mD7jjwe5PqISAAOFWJOB5LpeTer0uV1dXUq/XZT6fa7I4Bs5d\n1vIQQr+tksHntWufTeZMpVIqXtk4y1u+X/TLGw6H0ul0dP+9v7+X79+/6z5sExKQ2HWMZ0aKEO9I\nUE8IK0JgEYTSitKzXq+nmZX2MCDCy8axYpsO4v1Gplu5XFbPXwR03YOae6ifTCYqSKEMsd/va0YT\nNmpC3gN7OMRBDo0z3R42yNTDPGUlxPmBzO5sNivFYlEqlYoUCgXN2H1LCemhcBuA2ed2D0b2cbFY\n9Nkj4jDoNiELylJFABme2wiaUxh+GfbnKvJYhJjP548Cbwj0I5hbLpfV79UNzr6GpxrJ2X3eLbEO\nsl0M+n5d2ylcko+xNJq8naDKQggSGEFrjw1K4/cBc8XaMJHTxrX8+0hCoZDaVFjxLKgnGBKmkGGJ\nJth47Ha7j7JEyfGBBDiRH+9rKBTSviO4e2az2UcZ4u6aFVRxEOTJj3suEu2Gw6Fm7kK8+v79u0Qi\nEd/5s1wuP7KFshWuaNqOgJy9t9iGxsfwe3ZO2KooBGWt+ORWRIj4ew8CN+FHRLSfFrLJ4/G4ig8Y\ny+XSNyfRzNwGXbF/WlEUMTVLJBLRJBckvMB+FTEZW7VhqzeCrHnIx4GKQPdO5gr42Wz2kb3leDzW\n5DT0kHCFCCtCYFgRAnPf9qBwBQhUROz7+t0/u0LdfD6Xfr+vNohwTWk2m2rJ1G63H83PY96LKUK8\nEbsh2xJ92xMCTYmRAW8zKzGJ2u32Iy/NY8wwvXRc1RPZIcViUarVqlxdXcnV1ZXPTz1oUxYR34UT\nmSLIkLQ+hJwD5L1wN0wbLMHmab2qsV5Np1OfSGYbUzOj9/TB3pVOpyWXy/lE1Lf6WL43dv5aywB7\n0ETGcb1el+vra7m5uVEvdmQhB+H2GbAHUddGw/4f8nNwYLcihLWcweURVkYIimWzWSmXy3Jzc6MX\nSXid2vFagrKN3WxOCBC49MZiMfny5YtcXV1JsViUZDL5Lj8jctq42cC22aEboHHPhDbQO5lMtJQe\n2clcZ8h7gwx3BH8LhYJcX19LpVKRXC6nCQjr9Vr9pyE4PDw8SLfbFc/zNAOVwurxg/MLzuwIwnW7\nXWk0GpLNZmW1WmmDaQwR/9kLf3bfa7uXLxYL7adlBat2uy3NZlPjHqiCxB0DSXgIKCKA5yZ6LpdL\nKRQKvmbW+LjnNCUmLwP3QbynzWZTQqHQI+uZ14LKBPS9CYfDKiJAUIAdk7VkQoa4DS5b0QDZ4C6o\nroHgEY/HVdiyvSpcQcP298J+zYSk48RNfFqtVjIejyUej6vV1mAw8FkxPWXHZPsiuZUQoVBIE6Ug\nUuAeg/3xqbXIdRaARS3GeDxW8aHdbuuj7f+ABIBT2YcpQrwBd/JhUbTWPBAhEMhBYxG7iKOhFw5z\nbH5zvLjlpvF4XLMyqtWqXF9fS71e1yAeFjr34olDIA5sWGSwaboLCecCeQ9cX3McwNC4CYd5iKYI\njODQhWypfr8v4/FYM0woQpw2mAvWvsgV0I8Bd/21JeG25BbZdLVaTa6vr+X6+lpKpZLkcrlnixBY\nn+1B1BUiuC6/DPvzcn3vcVlE9i0CD6FQSMrlssznc9ntdhKLxaTf72svLYzXElSyjcCxtSlxs++u\nr6/l6upKbSOe+31zzpw3QQKEFbZEHgsQEDzdbHMrQhDy3iCAiEa/tVpNbm5upFwuqwgRCoW0oTCa\nYMJ7utfryWg00uAg7yzHjw3IiYi+t71eTxqNhsRiMVkul1KtVqVarWplKdYwBIeDhAhrFwJr6cFg\noAEzN2g2GAzE8zwN7LoihMhf54Dtdqv7MxJmNpvNoz5fyWTykY8/7yfvAyoOxuOx9Pt9abVaEg6H\ntckvRM2XWhbh42ELizmWSCQeZZ+jMbXdW92EoaCA8b7G1O65Dw3OrZWTm/BihTURYVX0EWPXO8Rf\nJ5OJT1xHZbwVo4IaU9vm57YxtV0Lq9WqCl6IByeTyZ+KEG4FPkQIVKch7mLFh3a7rQmhSAoNSpo7\nZihCvAM2I9P1gkU2KbL77CIOX8RGoyGdTkcrIbBxHvvkuUSCmlNaEeLm5kbq9bqUSiUN5gZlvrlW\nHxAhUAlhFxJ8POcDeSt2/toDPayYUEZoe5egqaorQsznc8064SH/tNlXCQEf12MRIUT8fUwikYjO\nYdsQLEiEQKWPtUZ0sVmC1lriqUoI8nxcD1fXaxWVECJ/zcl4PK7WDLFYTDKZjGbj9no9SSaTT/oS\n/wxb/m8vIdZ6yQoQ9muq1+tSKBSeVQnBCprLwN1j91VCuNi+S+gRx0oIcmhstRn2ykql4qvkdkWI\nb9++aTDZihA2gEKOEzcoh8zgbrcrsVhMs93RNyKRSEihUNB9OegsaIUIrGG4J3Q6HRWt0DwV/eRs\nxnnQHQMCRCaT0cA0suVhJWXtEGElFlR1Sd6OrYTAWhCJRLQ/SDwef9PvPt5bCBDZbNbny49zuLV+\nwny01jU2kcg+uriV1AhMu9aZ1p4JtmI48y0WCxkOh6/+nsnhwZ0uFAqpCAEBYjgcqvWgTT4KSjhz\nmztjnQGhUMiXLIWkPutu8rNKCPs5ZrOZDAYDX+UDeqZgQMRFHBHJAKdyV6UI8QZcax4rQjzXjgmV\nEFCybCUEOT7cUntrx1SpVOT6+lqq1aoejJ7KCnDtmGwlhLVjIuS92CdCuJUQ9jCHzdr6xvb7fV+z\nI87T08auZRAh4JN6TJUQIn9dHKxlj20sl0qltD+PFSFsIPmp7yeoMaxbCSHCIPJrsT8/7IHWwgG9\nFXAhRfJGNBqVTCYjpVLJJ5K9RYAQkUfe/clk8pGo5f4ZgbtCofBkJUTQJeAULgbk9dikJNfia59f\ntohoJcRsNtNKCNtziXOGvDeohIAIcXV1JdfX175KCATmIEK0Wi359u3bo0x2ZAJznh4/tjpgt9tp\nJQSyb+fzuc6NQqEgy+VSg65BFkz27yFYoXHqw8OD3N3dae+H79+/y3g8fpS1DlsbVEKgahW2S2gK\njDuLyI9zK5I9bSUEzml4ffI+WCcPrAXoHYgz0VtAjwYISW4Spp13+ypx9j26c9YNHuPP9kyKgf3Y\n8zwV7ER+VEAMh8M3n0HJ4XDfe8Q0ZrPZI2vffUkiQclD++bTer3WZKlCoeBLLP6ZQO9W4qMSotVq\nyffv36XRaGjTdjxOp9NHYtsp7cH8zXkDCIAgU65UKkmpVJJ8Pu8TIOBZB48wBPOw2Q4GA21Ijc2W\nHB+w3EKwK5FIaCNqvPe2+Wk8HtfNyVXlsbjAYxULCnzdEOAl5D1BdjHmMPxecYhH5jvKW5GhDJEM\n9kv7ylvJaWLFKWtNE4lEnsze/dXAAs82k7MNXzE+ffokV1dX2gcik8n4Ml3sxcUe2pBNgvk+Go20\n5HU8HmtWsrWeIK9nvV7LfD7XpIxsNqvvhbWL2+12esnFpdf9mNdihQYrQLjDChG24vUpISTIZmc8\nHutZj2voeYEzIoQt7K1IRHLnia28whzxPE96vZ5a3WCu8DxI3opNoMJZsFAo+HooVatVKRQKkk6n\n9SzoJkyNx2Ot0nlOhic5XlDtPJ1O1YI1HA5rUB9nrel06uuLhMoIGyBer9d6n0XD1IeHB83gtb3k\nXOsRBLeHw6FaWOM8inMeqid3u51EIhH9e1iIIvHE8zyZTCb68UH9AMjL2W63el7rdrtqW+P2X0Bj\ne8wV7HtBAnzQ2Gdb+Ctwq6ARVEayUzqdlmg0qpURSMyDWGsHOT6wn72He4Nru2njg0jsTKfTmsyH\nu2cQuAuhqfp8PpeHh4dHo9fr6TqKO+kpJ8ZRhHglUH5t4AMHuHK5rIEP2/wE6htUVQx4q8Pv7hQn\n0qWA9xzBW7znpVJJG2ShOZa1MbEBXRy2Op2ONia/v7/3eazahYWQ9wKbJGyXIJ5BME0kEhKNRnUt\nQmYmL5zkGMDFM5fLqdhvRX88v7q6kqurKymVSpJOpwN92d3S1+12qx7G8OHs9/vy/ft3abVa0u/3\ntRH7c5qMkZ9jbT5SqZREIhEVOFHqnMlk9L0Kh8O+xoXwEC4UCq/+GhDocPuK2EcbeHHtmxAsCcJ6\nZHuepxZSCMQw2eC8gAgBAaJYLD6q2oEA6gbv5vO5rj/dblctWieTCZOTyLtgLe5gv1gul6VSqego\nlUo6X1GF5lYGWnsUrl+nD4RynI1isZi02221rtztdlIoFHwVg6lU6pFVyXq9Vssl2C9BgAiym7Zz\nB/vkYDDQz4nAHkRdEfHZ8MBzPZfLqT3idDrVfRnixmw2+5Cf67kBEcLzPP35LhYLXwVLp9PxNTRH\ngpvI4+oDeya3fUY+GgSXRf6qFrN2YCIinudpteJsNpN4PK62YshO59p43qByxw70iqvVahoPtonJ\n+6rwbQWZ53kyHA7l/v5e19NmsymdTsdXIXsOlfkUIV6JFSFw0bi6utKAtBUhUNKFQDQWLwgQk8lE\nGzPS+/V4cd9zlC9b4QlZb8gmxkZmM93g82YzRu7v76XdbstwOJTxeMzMN3IQrAiB7De3egdzFheT\nyWQi0+lUhVIGQ8hHgSB0Pp/XoAkq0YrFoopqsMkpFotajWgvOqFQyFf2iqAKejVZD877+3tptVpa\npQaBzrVnIi8HB+/BYKA+rNaGMBKJyGaz8TWIRmAkGo1KOp2WQqHwpiCDLcl2M4Wt7ZftD+E+f6rR\nufXIRoY7zgHM0DwvYBeCPbZYLEqhUAismLEiKPZaiBCdTkebt2LN4TpD3ort/QS/auyhlUpF7zIQ\nWFHFb5txPmVRSE4PvL/oAYEMYeyzyHTHPQEJTKhatEkcq9VKg2YIoMG3HFVd+3od4iyAPX+5XOoe\ni6pDV0Szgm+pVFLBFncYrKnkfbAiBJ5DgICrA6ypq9Wq1Go1tdIU8VsoQbDH+epYKq6DqjFgS4fz\nZzgc1tgdbLQjkYgMh0PtkUHh6/yxSXGFQkET4Or1uk+EgEDxVD9C9Obp9Xp690Qlma0mQ7U+bDpF\nTleAEKEI8WqsB16pVJJ6vS7X19dSq9V8WfEoB4QFDxZsV4iwPtSnPKHOGStC4D2H4lkul32VELbR\nkYg/oAvriU6nI81mUxqNhtzd3Um32/UtMIS8NxAhcGjHepXP57WCJ0iEmEwm2oiaTajJR4FDH4In\nNzc3etirVqt6+YGtDkpj7SXHXnQQALTer71eT9dkrMsICFprFPZCeTsIPESjURXqcf5BpqOIaNDM\nvqfpdPpdSt+DGtDZiygy9qxNGRIM7AhiXyUEKiKZdHJeuNm5thICYqiI+NYPBHZRCQEvdbcSgvOE\nvBXb6Defz0upVJJyuaz7J4ImNjvZFSGsaE8B4jzAPoQz/2azUQECdwDENSCsFotFnRtYx5bLpQoQ\nd3d3cn9/77vX2gbmIv7gGcR6GyuBBQ7cBxBzwV3cNn8tl8u+sxkcB3CGIG9nt9tp413YMiG5ApXI\nuVxObm9vZTabqY1mKpV6JEBgbRH5q/IAZ6+PxE1K2e12uq8nEgnZbDYSi8V8fTxXq5V+D2govK93\nCjkfUAmB+2i1WpWbmxsVIrCf4u7wVMISRIhutyv39/fy/ft3FSPQhLrf7+t9Z7VancX+SxHiDdis\n+Fqt9igrPpPJqCKKRys+2EoIcvxY4cl9z63wFORPbQO6yBqwdkx3d3cyGAwoRpGDgiCurYSAHZMV\nIVyfalRCUIQgH4m144EIgfJXHPyurq6e9Vq2cSH8rnEIfHh4kD/++EP++OMP314NCx3yPkCEsFl1\n1mrBCkjIQEPfGlvV8pbG6a6Pryto4BJq7Zmem7ln/bbh9d/v933Zo+R8QCVEKpXSPdbaMcViMZ0z\nbiVWkB0TqhBp20XeA2SSo2kmKiCsJVO5XPYFloOsmKwd0zkEQi4d1yMdFc+4sw4GA70voKcR1iQr\nSi0WCxUhEEjr9/uP5lMQuFvMZjMJh8NqwQQBAn1K0BdMRHyCL77mSCSiZ7ler8emwe8IzmmLxULP\nP4iDpdNpfUTPj0Qiof06XBECiRuuAHEMQoQVSETkUQVjIpHwCRCI19h+nx/9PZDD4ybF4S6KSgjs\npz9rfC3iFyEajYb8/vvvmgCHMRqNfvF3eHi4Oj8TXEJtQy8oX1dXVxoMKZfLkslkfH6E2MTR0Ry2\nOxAnyOlghad6vS71el09VBEkcRcZzANkQqK8qtlsSr/fVzsuljaTQ+M2H4Z3qm3YK/KXVYSb9cb5\nSX4VoVBIs94xisWi3NzcyKdPn3TtheCP8vznHv4xxyFA2KbU4/FYh+2HwqDx+4I1BplkoVBIhsOh\n9tJarVbieZ7k83m12CoUCmobZ62TXguaKeJyDVsKSzQafXTRRqNhjKcyLoP6j7ie2OT0cC+XyPrM\nZrPa8Bc2JtaOydowwaYVzQin0+kjC0QmpZD3IBKJSDwel3Q6rYFdNCCGsIuMeFRrLRYLGQ6Hj8R4\nOze5L54X1iLOJkm6orq9J2Afb7Va0uv11Fb4JXcHt0IM/RxGo5EMBgOfJVMmk1Gfftu8erlcqiOB\nbQhrqy+4lr4d/AztGRoiQq/X015s6/VaBoOBLwALKyac61Hhavsw4NHtx4W7qrXOfM+Av/3c9vu0\n/46vH3fodDqtdjtP9Qgjp4t9z3HmhyU7YsC3t7dSr9elWCzqHSFIfAi6D+DeibXO9o6z1kvnBkWI\nZ4LDG/wIM5mMlMtlzYa/vb2V6+trDYjYpj3j8Vj6/b5a77TbbfV6pQhxOrg9IWq1mtTrdT3IY8EJ\nYrFYyGg0kna7rTYfOKwFiRA8JJFDgAwUNFvFAc9ah4nIo8uFzXjj/CS/glAoJKlUSgN58Jqt1Wo6\nUHWIKp6XZL0h2IILFAQI2I8h6LJYLGSxWNA65wAgewxZlbvdTgaDgYj8VSXhlvrn83m9jNoLwWvB\n+24rvtz3OZFIaL8RDFw8cZHeJ0K4zTu5x58Ptrmm9duHFRP61dheYQjUWAHOCmF2PrqZloS8Bdxj\nIZTZKh00zURQ0TZZHQ6HKkSgep+WcucLbATn87n2z4IoYKv63GAaAs62t+Fz9zybCS/iFzxGo5H0\n+30952UyGT0zWKucTCYjm83mkQiBflP4ejlf34Z9r/C+I3lju91Kr9eTcDisFqfNZlNE5JEIgTMU\nHiEo2I9DY2s8ptNpX48uW1Xxq8DnhF2YtQt9ynKHnC42yQTDJqFDhIC7hI0JBglbbnUhRIjhcCiD\nwUD6/b4K/ufci5MixDOxhzfb0AsixM3NjdTrdb2UxmIx3UQhQiAAjUoIihCnxT4Rwm5A+zYfiBCd\nTkfu7+/lzz//lMFgIIPBQMbjse+iyQMSORSwLrFZHAjkIRPZDZi51RCE/ApCoZCk02kplUpyfX2t\n/R/QgBqPuVxOKyFechmxc9uKEFaIwNq8Wq1kvV5z/r8zuMDa9QYVEJPJRPr9vtox2IaYOGPZ5tCv\nxQpOrj0m9uJMJqMZTzi3FQoFzU56SgQJEiHs35PTxe6n8NtPp9NaCQERApmeqNQKsoLDGgQ/dMwz\n7L2EvBV7j0UlhBXIUAmBADQyM4fDoVoJu3OT++L5YftD2Ex3CBBoXO72UtpsNj5BH8Gz5+51tloh\nqBIClYiFQkFFCFR2J5NJXSchQtj7jU2kol//28HP0Z7hIKqHQiFZr9cymUyk2+1KJpPR/4dAbCwW\n84kLmUzG18AaA0kf5XJZPw/mHsSMQxI0V2yfsGQy6Ws8jKQ+2jGdF4j/If6by+U0/ov76e3treTz\neZ3T1n7TrYTA7wqSUHDftH1WkJiCirJzhCLEM3HLWJHlZEWIWq3ma0iMSghcplutltzf32umAO2Y\nTgtXhIAdk21Y+VQlhOd50ul05O7uTr59++Y7rFmrDx6OyKGwlRA2gGdFCBHaMZGPB5UQ5XJZbm9v\n5e9//7tcXV35gtHIjMIF4KWVELhgww7FVkPAjsm10CHvR1BjS9us2mY42oESflSnBvVhei6e52n2\n0WAwCPRdzeVy8uXLFw2+4XxnbQV+9n2yCuL8wHnfzlVrx1Qul6VYLKqFhG1MjeCNWwlhz4WcM+Q9\ngZ8+7rEImNg+O7YSApmZECGwJ04mk0drGjkfsDZhLsznc7U9tEPELxzY6lJXQH2uCGGD27YSAoHe\nQqHgEziw9mIPjkQiKqxZOyb7OSiavQ92H8NdMRQKafJtp9PZmyQSj8cll8v5hu21hXF1dSWz2Uw2\nm40K/rZPF/qAHPJ7dLEiBBIPWAlx3mDvzGQyks/npVwuawzQ2jHBhglzISgmaJNQ7LlvPB6L53la\nCeH2YDpHKEI8A9tsLpfLSalUkmq1+miUSiVfEy8c5KDid7tdaTabmmVJEeL0wGEHl0w0XPpZ0xnb\nmLzb7Uq73dbLp9vs9zUK+ntcAp77eW0z0Lc2BRUR30WGF5rDYntCIIMjKHvDtYqw85QHeALw+48A\n2z4RAP6tGEHZIS7xeFzLXT99+iR/+9vf5OrqytcjAgFoWIu9tBLCLfcfDAa+jM/ZbPayHwh5ETa4\nij3Q7ckQjUa1AtVaIOG9R5b5axkOh9Lv933vv0s+n5fdbqeCA3pAIej8swvCvjMC97rTxgYjrDUD\nMjyRMYePBUFWcLPZTPtCoD8JIe8JLMNwly0WiyrqImDi+v73ej3p9/u6L8ImjJw3TzWRPjQ2QQRi\nGO4spVJJz2bL5VLPnNibI5GIJivgvJBKpTTrWOSv+w15OwioWp6zPsRiMZ/FZpAIAUsnVLzgrC8i\nuu/uE+jdKh3XThj3WNxHbEzD/RpcbAUk9n18bUjoI6ePPa9jfcnlcipAuI2oa7Waz956333Urm2o\nLLRiP/ZaO2/PNe5CEWIP7kIEBR5Bkevra7m+vlbvL6izULYwbBkryv1xwaCX5uWAeYQMEswXuzG+\nZZFxN1z83VNfj/26nhJQ3H8Ph8O+LFRsvC/9ekV+LMbuxdv+XvD3430J8q3e5wk8n89lMpnoGobL\nJ4XTy+IpocD1mC4UCoEfg8AxLoQIeLiHfks0GpUvX77I58+f5fPnz1KtVrUpse1nEtRY/Tms12uf\nRV6j0ZDv379Ls9kUz/MYaDkSbOnyfD4XEdGqicVioQHg14JLwM+av1nvYzSwfo4wi/0u7U+YAAAg\nAElEQVQewT/YRiAQcq6Xi0vAzYZEk19UZe0TWzGPEOhFBc65+/+Sj8VW9EMkgwiBgAkSpkajkfR6\nPWk2m74+hgjkEnJorC0YnAjQHwx2nJiPOENau7FisSjValXtrzE+UmAhP4Dtm21+7vaECIfDvgpY\nVBvgY2HLFPTa2GNxTrOPGCLii2O494q3ni3J6eJWfcEauFqtyvX1tdze3qoDTqFQ0J4m9k5rsbE5\n2+8OCVDfv39Xq370pXNjeucIRYgA3CzvcDgsqVRK8vm81Go1ub29lc+fP8vV1ZU2KERQGZsmNjsr\nQqCU1Tb04mXjcsBcQsbwe4kQrtLvvo5dwNxLsZ3n+4QI92Oi0ajPyxFZoc/9Wu3z9Xqtyq/neY8a\nILv/h7wN11YuyBPYVnGhHN/zPJlMJj8N1JHz4mdBfZSo2kaXLtFoVC+NqB5D0APZIkEZI5FIRGq1\nmlYaVioVzZayDYkR5HWbq/+M9XqtJePfvn2Tf/3rX9JqtaTb7cpwOKQIcSRYf2iRvypY8P6/tTE1\nbLhwJtv3NQRVh6FMet8ehX3TZsonk0ldY/H9kNPEVhUisJtKpXwZkUFr6Gaz0eALRIjxeCyz2cxn\nzUnIexLUEwL9lLCG4uyHKohmsymdTkcDuRQhyK8A98P5fC7hcFg2m41Eo1FfP7BisSibzeZRANme\nR6vVqvqsRyIR2W63slgsOI8/GJu0K/JjT8Q9wMYprACBZuORSESSyeSjvdKew7COWfHJDljKBTW+\nRrIURC1yeeDcjrM71pNarSY3Nzfy5csXqdfrUiqVHokQ7rnPrcCBXRn212azqf2CPc+T+Xx+Ma4g\nFCH2YLPWIUKgEuL29la+fv2qGyEakIiIbpq4XNgSG1g8WI8vXjYuAytoYV7Bz/I9RAg09MRr4M/7\n/AxFHluk7Lsw2z4nWJjz+bzO/1KpJKlU6kVfLx6Xy6V0Oh2JRCKy2WzUooz9MQ5DUCUEDl1BlRBB\nIgQruC4DuxbsEyiDghouiURCfTPxmM1mfQJCUBA5HA4HNiR216PnrGFBrFYrGY/H0m635fv37/LP\nf/5ThsOhJgtQhDgObLk/1iYrYOF9fy3ImHNtEYO+BreR8M8qIWwPHlsJsVwuz7rE+lLAecjaMAVV\nQrhAhEAlRL/fl9FopGIY5wU5BEFJKLASiUQiGhi01rGohKAIQX4lSOpEP4DFYiHhcFjFBwwEqhEw\nDrLOnkwmPgFiPB5/9Ld38aCydD6fa5KJe8/AewsBAiIE+oTBqslis81R0eXG4vB8t9v5BK1SqST5\nfF42m43asJPLxJ7bYcNkKyE+f/4stVpNRbKgSgjbY9P2UEIlRL/fl4eHB/n27Zs8PDzoPrtYLC4m\nEZciRADWCw4BY4gQtVpNPn36JF+/fvUtjGiQg0XVbehlqyHczHVy/uyzY7Lv/2svnsjQtAvePgHC\n/XrsHN8XzLEfgyzkfD4vlUpFG/Nks9lnf712YZ3P5xKNRlWAQCkaguE/+z7Iy4CHJSq77CUUlRA2\nA2kymahP+nQ6lfl8TjumM+SpwP1L7JiCKiGSyaTc3NzI169f5cuXL/L161cpFAq+kud9DbzcbHc3\nUwrPn9NjwgV2TO12W759+yb//Oc/tVT/nBuBnRq2eTUaH7p2gm99/ef4rtpGwq4d01N7VFAlhH09\n7nGnC9a/ZDKp2ZRuJUQQCLq4dkwUIcghCbJPtFWFIvsrIZBE5/bsIeRQICFtuVzqWmoraovFooq9\nyFgPqoRAZjEEiEM2MibPAyLTPgFC5Mf5zsbZUKGQTqcln8/7KiGCXBbsOtbtdqXT6Ui329Xnu91O\narWa1Ot1TYC0AsRLkivJeeEmmCBps1arqQhRqVQe2QLvu4+4IsR4PJZutysPDw/yxx9/qOWh53ky\nm80u5gxIESIAlHpZH2s0HqlUKlIul6VUKunkg8LuNrlst9vS6XQelVqTywPlXKVSSa6urtTKYbVa\naWXMW0QINN3CQGDEBldcWyVYCdjxMxEClxjbiOclIoSr7tosFwS9RcT3vVzKYnwI3Pccqr49zOG9\nhx+nzfq1/plsTH2+4P1Gdi5KlWG/tq8kORqNqldmvV7X31+Rvy4FqVRKPn36pB6a19fXgSJEEPvE\nhddkiMAHGKItMqQQBOz3+wywHCkf7Ytqy6ntPHquZ6t7Qf7o74e8D/YchUbluIwGleTj0QoQ/X5f\n7wkI8lIAJW8Fc9NWjLn+6qlUyldFiPUNzdJHo5EvCYV2nORXgqAd1tHpdKoJnr1eT/L5vK61iURC\ncrmcWoRmMhkpFouaOIXq7tFopJVql+K7fqzYKtd9IC4wGo107SoUCjKfzwMrISzWgcImWbpW2MBW\nUz+V4OLa69gzIZOLzwNXsEfSJh4LhYLkcjnfHruvDwSSllBB3e12pdVqSbvd1sderyfj8fjiqg0p\nQgQQZDfz9etXub29lWq1KrlcThKJhC5oWERtM69WqyWNRkOazaZOLgY4LpdUKiXlclk+f/4s2+1W\nCoWCblwIAL5240LWuh0QNWzQzVY7oKoBmZkY+2xR7EXG+nJivMaOSUQ08w9B7tVqJfF43OfdSPuf\n12H7jyAwgsunrX6wfvpvzSomp4mbNdRqtXSNwgUvSCiIx+PaK2m9XkssFnv0u2pFS1TeQPTCnIPg\n4WKrumyll318DkgSmM/nehCEzzUCf1xjyHN46RqJigcE9ubzua8nGOfd6eJWk9q91CZ0uL2/kJE7\nGAyk0+lIs9lUSyZk7RLyFmC9iZFMJqVSqWgfCOzDFgQFUe2F/oa2Zw7nJvnV4Py33W5VSOj1etof\nIBaLSTqdVpEMyTGFQkET8CD8DodDSafTWvVqg8jk+EAyHOJrmUzGZw2M9chWlNpKhlwuJ7vdzrdH\noxn1drvVvnNIMkafxH3xEOAm6yGhlGe68wBWTKi6wbxAc/R9Zz0Rf9LUZrNRe36Mdrstf/75pzQa\nDel2uz6760tLvKUIYcDlEoGVer0uNzc3OqwIEY/HdZJhE0NmE0pY7+7u9HIxmUwuSt0iftLptFSr\nVdntdpJKpeT6+tonErxl44K/3Hg8lslkonMNpYV4dC/KsVjsUVOmoECj26g9Eon4MqoymcyrmjfB\njslWPazXa4lEIjIcDiUUCsl6vZbpdPqqn8ulg3JCXEJRjQMRwlZyUYS4bCBC4HKXy+X08pZIJCSb\nzQauTxDsIUDkcjnfv+92O5+on8/n9XDvzrl9/WuC7OX2Pd8HAn9YJ1EKS59r8hqCbAP2ARECF2kk\nCdDy6/SxmZbWMs5d12zGJAJpECHa7bY0m00ZDocqQnBekLcCESKbzUoul5NMJiOVSkWKxaIG2WKx\n2COBzFrOobErqvif6ptDyCFB0NeKEDaZLpfLqYALEWK73eodCAJEr9eTTCYjo9FI92HaYx8vtn/S\naDSSdDotk8kksBLCnsOsnRLmCAQIuAHsdjspl8vqcFIul9Xf/2ciBPZzrIsQaX9mz0lOA+yfmUxG\n76+5XM4nQriVMyL+KhnYgk0mE03ua7Va0mw2pdFoSKPR8CXDXeIeSxHi/2NtHxBMqdfr8vXrV/n7\n3/8u1WpVKpWKVCoVrYSwF8mgTNK7uzvpdDrieR4rIS4cVEIkEgkpl8sym80eLVav3biWy6U2W8Ij\nsgQwVqvVI3/1RCKhZWVQeoPEBLc0EeWudrzWY9OthEC/AStA0DP7dSAwgoaotnEmmmfCguutzV3J\naeM2o0yn074gxr5DEfbKaDQquVxOarWa/ht+Z8PhcGDF1XNEr6cqIdznT4FKCPQ4QQayFSG4xpDX\n8NQcDsosRqCE1nanj9vryxX0MTdsti0CaW4lBCo/KUKQ9wD7LrzxS6XSo0oI9GPD/LTrFSoHIULY\nhClCPgJbRYY+nPF4XAqFglQqFVksFipCpFIprf7OZDIqQLRaLU3EQkzGzn9yXKBnxGw2U2HB9id0\n3zPsuXYOoGcTPP5xH97tdj5Hh2KxqOIsXAKCsEkFbiUEz3XnQVAlRC6X09iJ26PQtd6EqIlKiG63\nK/f39/Lnn3/Kw8ODdDod7VHieZ5WcV3a/KEIYbCVEAiofP36Vf7zP/9TS3EwUAmB8is087J2TPf3\n99Lv92U+n2uwlVwmOPSUy+XAjIu3HH4Wi4X0ej3p9XrS7/el3+/7MpcwYMuDga/HjkQiEfg5gjI/\nX9sQ1mIrIVxRbzqdSjweZ3b+K4Ggio0U1S5uJcR7Nnklpwks3bB/oXomm80+GRTDHELJ8z6rJLte\nvHSeBVVCBH2Op7CVEMg+RoLAdDrl3kxezHPnsK2EgAgh8rY9nxwHthLCtWPaVwmBtRZrUafTkVar\npYHfS8uEI4fBJhGUSqVAuxGc/1AJaOeoWwkhwjWLfCy2EgJnukQiIZVKRe1MbCUE1uBCoaBxmXw+\nL5lMRhKJhK/6hxwnECGQkJhIJJ5lXQMRIZlM6uugyTDcH3a7neTzed9Ac+Gf3VOsHZONYbCi5jwI\nqoSAk0Q8HtdEuiDcykJUQtzf38u//vUvubu78yUOj0Yj3YMvbe5QhBDxZQLDfgJBu1wuF6iA4eCG\nhjmj0UhLbWDzgGbU9NI8H6ytAqw9bHVBUHMabGaHyjQPh8OarYysPGzQT1VCJJNJrYTIZrOSSqV+\naqu0b4G0DTvdJk12uCwWC+n3+1otNJvNtInPpSnC7w0st+CLCTscHMCxjrkNzGezmV4+0aQYTQkv\nza/wUrDZucPhUBKJhOTzeSmVSr49LEh8dP/8mp4NL8Ee8OxzO4cRzEOV1XQ6lW636xvNZlO63a6M\nRiP2hCAv5rnzxW1iyHl2PrjNC91yfbu/utYNOOfg+SVmwZHDEYlE1KamVCpJvV6Xcrks+XxeUqmU\nWq8iyIdKLdi64rxnM8TdpCN377d7Mtc58t4g8XOxWGgjdc/zpN/vS7fblXa7LeVyWWM18P7HGp3P\n56VSqUi9XteKitFoJCKi92R8HnIc2CSOSCSizclHo5F67FtbYRuHsbGYUCik8wB77G63U3cAN7sd\nBMU2ZrOZzrlWqyUPDw/SarWk3++r6wnn0Olh9zII+MViUarVqu6fEPD3VfJjrtq7J5KD+/2+9Ho9\nGQwGMplM9G59yVVYFy9CIDiMxcc263UtTKwPGPzsYV/R7Xbl4eFBG1F7nqcTzCqk5HSxByD0//A8\nz9f87bW2RG8B9kgoL0QTLnfjtA0UcXnG3I7H44HNdZ76s8VeZPCIQ50VQlyWy+Uj8W40GqkYccmL\n81ux88KKEDZIIiK+5ujobYPeIjjsYcN0L6XkPHDXtXg8rsKgbaRrPTCDRNVfMS9cYdPOXTwfjUY6\ndzFgwzQcDmU4HOq+PRqNZLFYcE6TV/HUvHmNdRg5HXCOwh4LkR9Z5lgjrde+tZ+0Fg4QIBjAJe9B\nUCVEuVzWhDqc/7D348wHf2rX7sQmU9nkAxuMea8+d4QEYe3CIPDibNfpdNRqDIl1uVxO70GJREJy\nuZxUKhW5ubmRzWYjvV5PwuGwBpbt3YZz9ziwIkQoFNIE0NFopBZbWOt+FodBT4jtdqvzJ5lM6v9x\ng8qYAzawDPEKNoqNRkP7v/I+cbq4+xmsDCHgX11dSalUUjv+fXMMla5I4oS7QL/fl8Fg4LtXI052\nyXPl4kUIEdHgrLWpgW96Op2WdDqthzZcLHa7nSwWC/E8Tzqdjtzf30uj0VARwg2kMsPpPECTJNvt\nPp1Oi8iPQ/9HLCaw3UmlUnrgguhlh632CYVC6nlny7JB0EHsOSKEzaDHImvFCZflcqnBQCjE4/FY\nLVJYJvt6MBdQCVEqlXyVENhEYSeHAIkrQCCgi3+nVcT5gd/fyWSiIqXneb5sDTSN3+12ew9gh+7f\n4lqb2Oxia2cCr034biJDyQ5kUvHSQF4K5woReVwJYUWIoEoI6x/tWlAycEveE1T1I5sTmZy5XE6S\nyaTu5VaEwJ3GFSFsgAb3B9iO2X5iqMZm0h05BFaEwHPbWwd3GwT30BcFwelcLiflclmm06lst1uJ\nRCIqQESj0UeJclyLPx6IEFhjXBGi3+9LLBaTTCajdxPbQw5gzUokEhr/2O12EovF1F4nKAYi4k/S\nQoNz2Cg2Gg35/v272rvyPnF6uPub3TshQlxfX2t/GVRCBIG5gjumrYKA/RKqZXAGvOS5QhFC/mrg\nCv/0oEoITDoM600IEQKNqFEJgQxSluGfBziwQ+WEpxs2PvQJ+dUg2GyDzkFNr4N82W0FEBZVK0A8\nV4SAPyd+LqPRSL1k7XBZrVYyHA59WcoQ8C69TO2tuFmaECFcuwgc8lCOj3JXG6ydTCa+qhpyXkBc\nteJCkAgBnvLCPDRBQb3FYuEbdk++v7+Xdrsts9lM+zPBmx9CKS8N5LkEzRPOncsEQQ1bCYH9FYkd\n1hsYDcrdRpZuFQTnE3krsGOylRA4A0Ik2+12slqtfHca7PsI5AI3SOM2YxcRDQ7bTGNC3gsID1aM\nQCWEFX8x/5LJpOTzebVXQSWEfY3ZbCae5+kd2Fr1kI8HZ/3lcql2wRBMIUIg9oL9eB+48+Lj8H+C\n+jgBG/eB+NHv9x9VQnS7Xb1D8z5xerj9vdy98+rqSuLxuPZB3HcHRmwYPb9sgq3tAWGrBi95rly8\nCAEBAsHbVCqlaheauKbTaYnFYr4sTDSihiKKgAcWRZTbMJP7vMCBHdkXmUxGRPwZcW/F9TEMerRg\n4YTHq8jrhYQg/+p9r2VBJpUVFOAta+19XDabjS/bHo+oIuLvz+uxdkzw93d7Qoj8VQlhBQhbDYHK\nFJYpny8QIUREsxhxWIIQMZ/PtVoQc8C1Zjh0Y3NcHG1GMeatFTtbrZbc3d3Jv/71L/njjz/k4eHh\nUcWE27eGkLewb11kUPl8sXcH2H88VQlh1yDXjokCBHlPbBJKoVCQSqWi/SBsJYRt/IozOBIPEMy1\nXuv7nov8lZzAZr/kULjnNdw7bUU/gsz5fF4r8OPxuGSzWVmtVr6serhZuPZ5FNGOA6wl2EOj0agv\ny7zb7Uo8Hvc5QriJcriXBMVKXILiHzbBElU37XZb+0E0Gg0ZDAa+wDI5HRAHxl6GyhrETarVqtRq\ntUd33SDLcsSGPc+TXq8n7XZbut2uVkIgQZf84OJECOv5hSBdoVCQYrGo4/Pnz/Lp0ycpl8uSTqcl\nFAo9yracTCZyf38vDw8P0m63tfphPB5rQy9uYOfFbrfThkSNRkOi0ajMZjOpVCpSqVQ0gO42d35u\nYM7NNMJmicURz59D0IUXG+tTvsNB/8/1eQ3aYJfL5SP7Hlgy2czjoM+HEkcMfDybIL8cK1bBbgsB\nErcBOWzlbJYHRCRslHgPuJadNzZbNxQKab8jCOzpdFpms5lWClr/VWSHIEPkkKDiygplyE6y1TuN\nRkMeHh6k2+3qngzrE2tVx8AfeQ5WYLOWJPvEN8wnevyfL7afHKqoUQERlFlpgxvu2sM5Qj4Sm2CC\nBq6o7imXy7LdbnXfh1OAmxhlE4pGo5H2DyPkkFiL5OFwKNFoVAOIuMcgMz0ajWpj4vl8Lvl8XrLZ\nrArH9pwoIrx/Hgl271yv1zKbzWQwGEir1ZJYLKaV/LC2QewE+7C1jnPPbNiLsS+jYtEmLPV6PZ9t\ndLvdlvv7e+l0OjIajVSw5XnvdLBzAJbVdvzjH/+QT58+SalU0v3OPevbeYMYGXpAtFotub+/l/v7\ne2k2m9Lv92U6nVKcd7hYEQJZHMlkUorFolxdXcnV1ZXU63X59OmT3N7eSqlUklQqJaFQSFarlZaA\nofwPzWjgOT0cDjWAeuk+X+cISgH7/b5Eo1FtgGp93iaTiaryIs8XIEREs4rswIEfdmAvESEgAKBP\ng9uoOuiAhcxiKx5Y72Jk7bms1+tH2ciwHbAj6GcKYc9ao+Djacf0fIKstqxVRKFQkFwuJ+l0WuLx\nuGavQYQYj8cqQqAaBYcrwPfiPEFGpA20QoRAP6TxeOyrDsRA1SAykQ6JtUFEoy/bbBqZSr1eTwc8\nWoPWP14YyEvY15DVsq+akPPsPLBnO9tPDs1P4S+N+eFWku4TIezHEPKrwbzDPo4K2nK5LOFwWHK5\nnGSzWR1uz7nFYiHdbtd3Xybk0FgRAudPZDDbqnr0gEilUhKJRGS1WvnuRKlUSubzuS84TY4HrE9I\nXBwMBlqVbb31UQGGakRUb6PSxe1jY/vLYS7ZWMZ0OpV2u609IFAFAVEC/RJ5nzgd3HN8KpWScrks\n9Xpdx9evXzUObEUI/H8Rf48aiFeogEA1/rdv36Tb7cpgMFBba/IXFydC2EsDVHGIEF+/fpUvX77I\nzc2NZrdbEWI8HqsiCiXUrYRA8JQixPlhKyEgQKDbve2BABHipUIESvvtyGQyksvl9GLwlN+hBZvp\neDxWn1cskngMWgzxf+xwhQS3eZfIj40c/2b9jn8mXtgDxHP/D9mP3VitCIHmhLlcTlKplDamtp7A\nsBjr9/tako8KGq5l5w3Kz1ElsNvttN8Rmvd5nqeWIxj5fF6rJ7DuHXKuIIPN8zzpdrvSarU0O8kO\nWyUBf2sbMEEwUISBP/I8ggQId5+3F1F3vpHTJ0iACKqEQFk/1sMgUSpIoOI8Ib8a167V9paDnU0i\nkZBSqaSjWCz6zu0QHcLhsKxWK5lOp77+UoQcCghgEL0gLsCdAJUQIqLrcjKZlM1moyIEKiHQW2C7\n3e71fCe/FrdKCwmPsD+yvVft2oXeTOj9ICKaxGljMqh+wLB9LUejkXieJ81mU5rNpjw8PGjiMeIj\nk8lEk/WYcHIauI44ECE+f/4sf/vb3+Rvf/ub1Go1qVarUiqVtJk5/i+Ak4Tt9QURotlsyt3dnfz5\n558+W2tWQvi5OBHC+ltCLS0UClKv1+XLly/yH//xH1Kv1309IWDHNJlMpNfrycPDg6/MBiLEcDj0\nleZwITovIEJAkIrFYqqEw3posVjogvVSn3S38iGZTGqAD16Hz8VmtyMz2BUTghZDlDkiIx7VPW6l\nQtDPxs2MshftpwIxT308s1Gejw2QWRECpYauHVNQJQQyy11fYHLeIKMDYgR6QkCAwLpQLBbVvrBQ\nKKi/LgTTQ+95thKi2+3qpaDdbvtGkJgZFOzjHk1ewj4Rws14d/cxXk7PB1eMggiBSgiIEAhiue9/\n0HmH84J8JG6gz1ZC7HY7SafTUq/XpVar6bAVzEgMWK/X2pCTQVzyK3D7mc3ncymXyzIcDvVuvlwu\nfZbGtmICdkxIzrL9e8hxYNcn2DHtdj96NcAOyfbAyWazsl6vJZ1O653Grkf2z/b9Rm8cz/Ok3+9L\nv9+XXq8njUZDY3739/fS7XYfJU4y2eR0cGMlqVRKKpWKfPr0Sf793/9d/uu//ktjwNls1mc/aLFN\n0xeLhcxmM58dEyohbIIuE2v9XJwIgSoIBHnz+bwUi0WpVqtSr9fl+vpaarWaXiqQ3YmFz2ZgwobJ\nNu8k5w2yfvBe2/ccC9JrRQg7L9E8ztoa4cD/HDBP7YD4gNcLEiGQYWCHFSGQdUCOH9cqApk+thfE\ndruV6XSqtmKo9PI8Tw/vFCEuAxsMw7wIh8O+xpWoLMBzVMpA3EdjQJtlEvSIz+EGbN0AnV07Q6GQ\nHvC63a6WR2NYEYKQ98TOb+uLDt9hkb8uyHbgXMjq2PPkudWuWM82m42KorbPFiEfgSukJZNJX+AO\nyU+ZTEaq1apv4Bxgm/y63uuEHJrtdqvnUPQGGA6HGkDudrtSKBQ0qIgEVAQYbVUv7JhQ2X/oyl7y\nclD5gvdoOp1KKBSSVCqlcZN4PK4CUzab1dgH+tbhTozEUms/PRgMfNXV3W5Xe8w1m01ptVrS7/c/\n+sdAXond85CQnk6npVAoSLValevra/ny5YtvnuBe64I5CPEBbjmYN3geVIFPfnBxIgQyPLDxVKtV\nKZfLks/ntSQPkw8HKpG/JhsauNoyPzbQvVyQheF5nkSjUdntdtqY+qUiRCQS8TV6TSQSvkMSMjae\nw3Q6VY90PEJAecqOCRnxaDJtRRAGUU4Hm3WJgAcO6Cg/haBlA7rIKu/3+9rQl2vbZQLxYTab6cXM\nChKe5/kaQ6N6ynqj24bVGCiJts3fbDmr7QnjihCTyUQeHh6k0WhIs9lUG0TsxSx1JYcgHA5LIpGQ\nTCajl5VSqSTZbFYSiYSKdbYn0nQ6VetOz/NkPp9zLT0zkEGJIAaCshCrsA9j/8X6hnsDK6bJR4Ps\nYWR+x+NxyeVyUiwW1WIW9+ZUKiW73U77I9r7Ra/Xk06nI57nyWw2Y8Yn+WXYu46IaDDw/v5eYrGY\nrFYrqVQqUq1WpVKpaDJWIpHQ5uvX19ciIlolAfcLcny4PexQqQ276sViIfl8XgdiKLBnwv0EtrN2\noK8c+swNh0P19Kedzulj+2ViHsCSzYpY6Mv6lKAO+65+v6/JvqiUQS9CWyHDs95jLk6EQB8INC7C\nZdKKELAqgaeriP+yESRCcHJdJvAkHI1GIiJaFvianhC4vNqyUVRFWJX/OcAjczqd6rDB6H2NqSG0\n2ewACBf7/g85HtxsdmsPhwDyer3WjXWz2fj63CDTA6o+KyEuExzyIQQg28wKEKlUSucJghH9fl8z\nzqylITLHRUR7kbhrjdvHARlOIBQKyWw2U9EMlYgQQ9B8mpD3BpZjyJiqVCoqQiCgAWHXBufQ0BC2\nhpyf54NtSohmlpPJRC+4SNpAgAwiBKpJrU0cIR+BtV4SEc0KhVAG0QzVjsgMxxkAZ0cEYbDWUXAl\nv4qg/l6TyUS63a7EYjFtZPz582fZbrcSj8elUCjo/TqbzUq5XJarqysNGC6XSxmPx6yEOELcPRXV\nDIPBQEREffmtbWyxWJR8Pq8xFMRVNpuNVszAfgl3GjsgTlCEOH3cfplISndFiOdU9W02G5lMJtLv\n96XZbEqj0ZBGo6E27a4IQR5zcSIEykvz+bxUKhWp1WqBlRCYfLYSIkiEmM/nan8UQrIAACAASURB\nVGtCLg+IECKiBxd30XquCAHLB9joQJRwx3PAhdeO5zTLxKXaDgSxaR1w3OCi6HqTWxsI1596uVxq\nBlu73VZbGwSGcQEllwdECOx9CLTZtWgwGPgECJS+F4tFvQCgLFpEtBQerzmbzfSgj0oKZCF5nvdo\nHUWpvR3w/WUlBDkUtu+JrYRIJpOPKiE8z5NOpyPdblebGLIS4rzAXmvXRlRC4HJr+9Bg/7UVX6iE\n4JwgH4W1XEJQ1vqcQyiDFSvOhai07vV6em6EjScrIcivxAal8Yj+nbAV9TxPttutJBIJ7WWGil1U\nQuD8iAoIOBqQ48OKTtvtVm2xl8uljEYjabfbUiwWpVQqSblcllKpJMViUYPOGOv12nfvbbfb6gCB\n9c7ehRHvI6cL9jxYD6JKBglzqI5we78FYZukN5tN+fPPP6XZbPoqIYJ6EZK/uFgRAhtPrVbzVUJg\nAor4g8cIyNCOiVhgx7RarQKbH70Ut3oiqAHmc3AbIT53IQz6f/b/cBE9buz741ZB2IEL5nw+94kQ\nqISwH8O17TLBXMHa5o5wOCyZTEYFCAgP8IyezWY+Czdkn2BtsV6aw+FQqxswer3eo/UOhz7blwIX\nBFZCkEPhihCVSkUKhYImDNi+YbZpeqPR8IkQnJ+njxX73UqI6XQqqVRKGxDasxQqyVAJQTsmcgyg\n6jrovoCBXky9Xk9ms5naMaEBJ9Y5iBUUXMmvBHMN58XJZKIVEEiOQQVEvV6X1Wol4XBYksmkxoJw\nJkVmMwKR5Phw7ZgQnxuNRpo8XCqVpFKpaELTaDRSS2s8rlYrX9PpRqMhk8lEEzexZ9PT/3ywdkwQ\nIWxzelRCPAdrx9RsNuXbt2/S7XZlOBz67JjIfs5ahLDdzzGy2awUCgUVICBC5HI5SSaT6gHmYjOa\nXEsbXiIuG1YIkGNjt9vJarXSA/XDw4P2E7FZbvP5XO7v76XdbstgMNCgrts0mFwezxUebaNzCLLW\namk0Gj3yZ91ut5o1ieE29EJ5tchfl0t8DpuZhMsCBTNyaGxgDkFkO9DAEGXZNiuKgbnTBwIEgD0d\nRNREIqFNfdH0cLvd+iwe4LMPSybOCXIIkCA1Ho+l3+9Lq9WS5XIp2WxW552tlg3qIYZHz/MeNWtF\nlVe/39eKRLsX89xIfjWYc0icgbCw2Ww0GD0ej2UymUgmk5HtdiuxWEwymYxaq/R6Pclms5JKpSQe\nj/t+V2wyH/l43Kp/i7VURxLAaDSSdDqtWe+r1UqazaZvLYPlkq0EI+cDeq/Chq1arUqtVpNisSiZ\nTGav+GgdJTAvsB9iT4QAMR6PWTXzTM5ahID3l7WPsL0garWa1Ov1Rw0GCSHk1HAPx8vlUobDoTw8\nPEg4HJblciki4guaLZdLbUbteZ7Pw5AHbvIzbEUDDvuw+ZpMJjIcDiWXy/kO/rj8oZIBVQ3IVoIo\nMR6PfZ8LPUxshhICefaiSMh743r6TyYTrXyw2b+tVss34JFu/WHJ6YN1Bhac3W5XotEf1ylURtu1\nCIFaiBGz2UztPzgnyCGwWZoPDw+SSqVkPB5LsViU+Xzus12C9apd4+zahmpFWC+i+rHX68lwOJTp\ndOrrc8K9mHwkbqY8Elam06l6/CMIHQqFtF+ZtQ9FZjREDPbwOS2QZId7xHq9VvtM9IVYr9faCwJ9\n5biGnTfhcFgt+avVqtze3srV1ZWUSiUVIYLY7XZqu4mB2Akal+Peiv2VZ7ufc/YiRCwW00UHfoAo\n06rX61Kr1dQPjCIEIeTUwcFpsVjIYDCQcDisXvoi/obV6/VaL5We5/kaUfMARn4G7Ehms5nPcgR2\nDShvTSQSvoEDnR042CFTGL12LKi4cDOVbN8aQt4b26gdIgSaUEM4G41G2gsCA5l1sO7k/Dx9rB0T\nMs1jsZiuTTZzEnaaCN56nieTyURFCF5UyaFYr9daCZtOpyUSifj6GIqIbx9GdeF0OpXRaKSCGR6R\nMIBhm7ZChOA+TI4Bt3ExEmWQ+OJ5nmQyGV3LYcEymUzUniWdTksqlZJQKCSr1cr3uuT4QS8bWGXO\n53NNRo7H49q0HGsZbJiQoEcR4jyJRCKSTCa1t9vt7a1cX19LuVyWbDa7t+/qdrvV8x4S5SBCoLE5\nmpcjQY5rxc+5CBECDUjS6bRaMUGEqNfrviAJRQhCyKliD00QHpbLpQwGA3l4eNCPsf6/9gLqBsp4\nCCNPYcvdIUZMJhNfBWI0GlVrEjzaZq22xNVmZAaVslr7CGsjQeswckhcEWI6ncpms1GLEogOg8FA\nM+sGg4GMRiOd0ww4nw+2EmI0Gmkw11puWUsmBHUR0IUoxUoIciisCBEOh2W1WmmG5m63k0gkIuFw\nWCsREaBF/wdUOkBItWdE2CDaJAK8Lvdi8tFgv8YZMRwOP6qEyOVyGoyOx+MSj8dlOp1KoVDQxNRU\nKqXzGKIGOQ0gPECMgNU6bJpgI/tU/weuYeeHrYSo1Wry6dMnqdfrUigUJJvN/rQSYjwe65n/4eHh\nUSUEehNikKc5exHCbUCCSghrx+QuTIQQcurgcIUKCELeGxzYkSlGyDmCLHcrtC0WC+l0OvLw8KDD\n9jhBhjA5XxaLhV5OITCIiN4pIMRa6zlWQpBfgbVjgiABoQD34lgspuIY1iz0j7BjNpv5Kg+RYU7r\nTnKM2PmIQKAVITzPk0KhIKFQSOLxuCSTSclmszKbzR41qoUFExJtyGmAIPBisfjoL4UcEUGVEJVK\nRSuf9okQSLhDjyX0EoEIgUoICpUv4+xFCNgxQYTIZDJaeofMTDStDoVCWmbtNu1y/aetys4DGCGE\nEELI+YGy/U6no9Ymq9XKZ78Eqx1kGzML6vxBcAqZ5hAjut2uRCIR2Ww2EovF1F4OlnOokmHzQnIo\nkBywWCwkEomIiEiv19NKxMVioV7oEMnQmwmVXOhlg/XMtSqhAEFOgd1up+vuw8OD9gMol8tSLpdl\nt9tJPB7XPhIQkJPJpMZ90OSYEHLahEIhTRSJRqNaJYM4sIg82t9Wq5Xuj71eT9rttrTbbRkMBj6B\nn7yMixAhYMdkS+yQBWLFB2D9BK2aCpsIChCEEEIIIefPer2W8XgsnU5HwuGwBiZs5YPnedrIlRnu\nl4HrPY4+IRAgZrOZRCIRTWJaLBaaTTcajTTDnJD3BtVb6K0EsWy73eo8zWazaq8U1BMCFV9Yz9x7\nL+++5BSACNHv9yWRSIiIqCi83W4lHo9LNpvVuW0TWBOJhKxWKzplEHIGIN5rHXCsE44VIeyet1wu\nfSIEqiAgQiyXS+6Hr+AiRQhbCWFFCDv5XJ9q6xtHEYIQQggh5PxBJUS325XVaiWe58l2u9XMdgz2\nf7gsbKNS3ANGo5HOjeFwKOFwWO8SGIvFQqsjKEKQQwBxLBQKaVUE/NFRrZNMJn1rFu66CNCi9wMs\naYIECN5/ybFjRQiRvwSI3W4nsVhMstmslMtlrV6ElXcymdR+AqyEIOQ8QNWD7VUYVAmx3W41Ed0V\nIVqtlrTbba2ApgjxOs5ahAiFQo96QljfL0w+fCyw2U02g8k2zKQQQQghhBBy3qASYrlciud5Eo/H\nNdPYngntuZAixPljbVtDoZDOgfl8LsPhUKLRqM/iFQMfh3lDyHuD9cnaMkGAiEajEovFJBKJ+OZl\n0NxElY8VHXjfJacERAiRH318kL0MAaJSqWhTYhFRO6ZEIqHNq1kJQcjpYyshIDaiEsJNRrf7IfrB\nWRGi0+n4EpDIyzlrEQITDNUQqVRKqyCwqdgJh8fVaqVK+XQ61Yk3Ho9lOp1qKb71xiSEEEIIIecF\nLExgbUIIcJug4rJKyEeCin6KXOTS2e12GreZTqfa06lQKEipVJJKpSL1el3/fbFYaH8I166bEHK6\n4LwGgcGN5eIs5yaiTyYTGY1GMhwOZTAYSK/Xk36/76siZCz45Zy1CPFcbAYbyu4Hg4GOfr8vd3d3\n8v37d2m32zIcDrVMFSWunHyEEEIIIYQQQgghH48NPor8VRHRaDQkkUho/KfVakmz2ZRWqyX9fl9G\no5Emn1LQI+S0QULRaDSSfr8vzWZTttutZLNZyeVyEg6HtQ/MZDKR8Xis/eDQA2I8Hqs7DpPR38bF\nixDIFoHVEvx+2+22NJtNHfD/arVa2oDQNqrmBCSEEEIIIYQQQgj5ePaJEPf397LZbGQ8Hst2u/Ul\noA4GA+2NguoIQsjpst1uZbFYyHg8VlslkR/J6JFIRBvXo/qh3++rWNHpdNTKbbFY+GxYGQN+HRQh\nTP+HxWIhi8VCRYi7uzv5448/5M8//5TBYCCe58lwOBTP82Q2m6lyThWMEEIIIYQQQggh5HhA757d\nbqciBNwvOp2ObLdbnw33dDr12a1QhCDktNlsNrJYLHyVEGhSnUgkJJvNisgPEWI8HuvHNBqNwEoI\n2++LvJyLFyFE/vJxXSwW2v+h3W7Lt2/f5H/+53/kn//8pypfGLYTOht1EUIIIYQQQgghhBwHNl4T\nCoVUhBiPx9JutyUa/REOQ2aztelGoJFxHkJOG9gxoRIimUxKNBpVAQINpm0lRJAIMZ/PZb1e+9YV\n8nLOWoTYbrfaZHoymYjnedLv9yWRSEgsFhMRkdFopN3N0Yy60WjI/f29PDw8SLPZlHa7LfP5XDel\n9XpN1YsQQgghhBBCCCHkyEHiKJJKCSGXAeyYRqOR9Ho9iUQiEg6HJRwOa5XUZrPRPsB3d3fSaDSk\n2WxKr9dTJxzGgd+HsxYh1uu1TKdTGQ6HEolEfGU4vV5PGo2GZDIZWS6XviqHXq8nd3d30mq1ZDQa\n+ZqPcNIRQgghhBBCCCGEEELI8WJ7QkSjURUd5vO5uuB8//5dG1FjdLtdXz8IxoLfh7MXIWazmYTD\nYdlsNjKbzWQ0Gkm325VcLie5XE4SiYQ2pMYjRAqoXm4HdJbdEEIIIYQQQgghhBBCyHECwWE0Gqkg\ngWT1drstuVxO8vm8rwew53kyGo1kMpnIdDqlCPGOnL0IMZ1OfSpXIpHwjWg06mswvdlsZLlcynQ6\n1bFarXwCBEUIQgghhBBCCCGEEEIIOU4gPOx2O+37MBwOJZFISDKZ1NjwfD6XxWIh8/lcn69WK1ku\nl7JcLilCvBNnL0JAgAiFQoHD4jaatg2JCCGEEEIIIYQQQgghhBw/ECGWy6WIyN7YsE06d4cIG1G/\nF2ctQogIKxcIIYQQQgghhBBCCCHkwmBc+HgIP/Pjkgf9Ksgp8ivmBOcdcTn0nOCcI0Fw3pFfDfdY\n8hFwrSO/Gq515CPgWkc+As478qvhHks+gifnxHNFiN/e/nWQM+O3M/kc5LT47cRfn5wmv53465PT\n47cz+RzktPjtxF+fnB6/ncnnIKfFbyf++uQ0+e3EX5+cHr+dyecgp8VvT/1j6DklKaFQqCIi/y0i\nv4vI/D2+KnKyJOXHpPq/u92ue8hPxHlHDL9k3nHOEQfOO/Kr4R5LPgKudeRXw7WOfARc68hHwHlH\nfjXcY8lH8Kx59ywRghBCCCGEEEIIIYQQQggh5KU8146JEEIIIYQQQgghhBBCCCHkRVCEIIQQQggh\nhBBCCCGEEELIQaAIQQghhBBCCCGEEEIIIYSQg0ARghBCCCGEEEIIIYQQQgghB4EiBCGEEEIIIYQQ\nQgghhBBCDgJFCEIIIYQQQgghhBBCCCGEHASKEIQQQgghhBBCCCGEEEIIOQgUIQghhBBCCCGEEEII\nIYQQchAoQhBCCCGEEEIIIYQQQggh5CBQhCCEEEIIIYQQQgghhBBCyEGgCEEIIYQQQgghhBBCCCGE\nkINAEYIQQgghhBBCCCGEEEIIIQeBIgQhhBBCCCGEEEIIIYQQQg4CRQhCCCGEEEIIIYQQQgghhBwE\nihCEEEIIIYQQQgghhBBCCDkIFCEIIYQQQgghhBBCCCGEEHIQKEIQQgghhBBCCCGEEEIIIeQgUIQg\nhBBCCCGEEEIIIYQQQshBoAhBCCGEEEIIIYQQQgghhJCDQBGCEEIIIYQQQgghhBBCCCEHgSIEIYQQ\nQgghhBBCCCGEEEIOAkUIQgghhBBCCCGEEEIIIYQcBIoQhBBCCCGEEEIIIYQQQgg5CBQhCCGEEEII\nIYQQQgghhBByEChCEEIIIYQQQgghhBBCCCHkIFCEIIQQQgghhBBCCCGEEELIQaAIQQghhBBCCCGE\nEEIIIYSQg0ARghBCCCGEEEIIIYQQQgghB4EiBCGEEEIIIYQQQgghhBBCDgJFCEIIIYQQQgghhBBC\nCCGEHASKEIQQQgghhBBCCCGEEEIIOQgUIQghhBBCCCGEEEIIIYQQchAoQhBCCCGEEEIIIYQQQggh\n5CBQhCCEEEIIIYQQQgghhBBCyEGIPueDQqFQRUT+W0R+F5H5Ib8gcvQkReQ3Efm/u92ue8hPxHlH\nDL9k3nHOEQfOO/Kr4R5LPgKudeRXw7WOfARc68hHwHlHfjXcY8lH8Kx59ywRQn5Mqv/9Dl8UOR/+\nl4j8nwN/Ds474nLoecc5R4LgvCO/Gu6x5CPgWkd+NVzryEfAtY58BJx35FfDPZZ8BE/Ou+eKEL+/\ny5dCzonfz+RzHIxQKCShUEj/vNvtZLfbfeBXdBb8fuKvT06T30/89cnp8fuZfA5yWvx+4q9PTo/f\nz+RzkNPi9xN/fXKa/H7ir09Oj9/P5HOQ0+L3p/7xuT0hWFZDXH7FnDjZeWfFh33Pyas49Jw42TlH\nDgrnHfnVcI8lHwHXOvKr4VpHPgKudeQj4LwjvxruseQjeHJOsDE1IQcAFQ+ofrB/JoQQQgghhBBC\nCCGEkEuBIgQhB8IVHihAEEIIIYQQQgghhBBCLo3n9oQghLwCCg+EEEIIIYQQQgghhJBLhpUQhJwQ\n7ClBCCGEEEIIIYQQQgg5JVgJQciREwqFdIg87jdBCCGEEEIIIYQQQgghxwpFCEKOGIgP4XBYn+92\nO9lutyJCuydCCCGEEEIIIYQQQshxQxGCkCPHChHhcFgFiO12q6IEIYQQQgghhBBCCCGEHCMUIQg5\nYlwBIhz+0cZlt9uxPwQhhBBCCCGEEEIIIeTooQhByDsTDoclEon4RpCtEkQEPOLfrOBgB15nvV77\nxmazkc1mI9vtVrbbrT53Hwkh5Nix66Ltf0MIIYQQQgghhJDThSIEIe9MJBKRRCIhiURCksmkJBIJ\nFSOi0ahEIhEVFqw4gX+PRqMSi8UkGo0+EiZERJbLpSyXS1mtVvo86M/uIISQY8YKtSI/xAcKEYQQ\nQgghhBBCyOlDEYKQdyYSiUgymZRMJiPZbFYymYzEYjGJxWISj8d9AoMdsVhMEomExONxFTGsUAHm\n87ksFgt9nM1mgWM6nUooFJLNZvOBPw1CCPk5VoDA42630wovihCEEEIIIYQQQsjpQhGCkHcGlRDZ\nbFaKxaIUi0UVFTDi8bhWRKBKIpFISCqVklQqJel0WlKp1CMBYrfbyXQ69QkNk8lExuOxjEYjGY/H\nMh6PxfM8FSBYBUEIOXbcqrBQKKTiA/6NQgQhhBBCCCGEEHKaUIQg5J1BJQREiGq1quICRiKRUGsm\nPKZSKa2cyGazks1mfbYkIiLb7VYmk4kKD5PJREajkQwGAx3xeFwDeMvlUmaz2Uf+OAgh5Fm4PXFE\n/JZMhBBCCCGEEEIIOU0oQhDyBsLhsK+6IZFISKFQkEql4hvoDeFWQtiRSCQknU5LOp2WZDKpYoIN\nwm23W0kkEtpo2ja+DofD2k8iHA7LdruV1Wol8/lcxuOxvg4sTgh5C7bBeigUUvENQlsymZRIJCKr\n1UrW67WsVqu9g1wWWO/i8bgOWNZhRCIR2Ww2sl6v9XG9XstisdA+N4vFwmfXRMGCEEIIIYQQ8laQ\nFGX7ebrY2IprH4tkUtyTrQOGdbrAx2y3W9lsNrLZbPQ5XpvxG3JOUIQg5A1EIhFJp9OSy+Ukl8tJ\nPp+XYrEo5XJZyuWylEolKZfL2g/CBtuCekIgeIsAnYg8Eg8SiYR+bvta0WhUhZBQKCSr1UoWi4VM\nJhNJJBKPNjQG68hbsNY5qOjBfMdjPB6X6XS6d0wmE4oQF0g0GvWtm7lc7pFIG4vFVHjAmM/nMhqN\n1HpuNBrJcrn0Hdq5rhFCCCGEEELego21IEHKxd5BkDglIr6+nqFQ6FHCFV7LihE2aW+5XMpqtdLX\nxutSiCDnAEUIQt4AbJRgu1StVqVcLmsviFKpJMViUaLRqG9AAbc+6FZEwEYlIr4sX2w8biNrbJKJ\nREKSyaRst1uZz+cymUwknU5LPB7XQJ5V1Al5LcjqwJxOJBJSLBbl5uZGbm9v5fb2VtLptAyHQx2D\nwUCfi4isViuZzWacixcGRAism5VKRfvgoJomkUjoQRyH8slkIt1uVzqdjoTDYRWw1uu1iAizhAgh\nhBBCCCFvxsZbksmkxmYsm81G7yu4myC2Y21mk8mkb0SjP8KwtlpiuVzKfD73DbyeiPC+TM4GihCE\nvAFUQpRKJbm6upJPnz5JuVyWQqHgG/vK+az6bdV22CpZ8QGP+DiU/WFTxAaZSqVkvV7LZDIRz/Mk\nlUqptZPID1HDLQEk5KXYSgjMP4gQ//Zv/yb/+Mc/JJfLSbvdlm63K+12WwWxUCgk6/VaptPpR38b\n5AOACIF18/b2VvvhpNNpyWQykkwmfZlAq9VKPM+TZDKp82cymfgyg7iuEUIIIYQQQt4K7rjJZFLS\n6bS6UVhgFYsE091u57Nfwl0Zltu458DxwooQ8/lcptOpRKNRtWdi/IacIxQhCHkBrrKdSqUkl8tJ\nuVyWer0ut7e3Ui6XJZ/P+6xGbNUDXsfaLNkNCx9jbZOsEGEz0FHeZ/tBJJNJmc/nMhwOJZfLSSaT\nkVQqpcLHdrvVzGFCXoudc/F4XFKplBQKBanVavLp0yf5+9//Lvl8Xg9ttln6dDrV5uz4XSDnD9Y2\niBCFQkGq1arc3NxIPp9XISKbzUoqlVIBAo/9fl82m432uYFQsdvttAyaXAZ2L3WrCvEcJfIQ7D/y\naw26ONp1j2sgcXHntju/w+Gw72wYdF7kvDpN7PrmZtPiPbWPQfPgVHDnuIg8ms+EEPJW3DiMu6fa\nf8Mj7iR4TCaTj14XdrEYi8XCl6iH+3I2m/UNmyD6/9g706a2liVrJyCE5hEB9rm3T0f////UHXFs\nA0LzLDG9H+77lNdObeEJrC1cK6JC2MayZBVVmblyreQR21kcMXK5nC2Xy3D203gVEXHoiCRERMR3\n4ujoKOFbTuf35eVlwoapWq2G4dLq+ecHTOMbSPGMP/MXEn+X38P+hsuJ3z85OQmseqlUskqlYtVq\n1er1ujUaDVssFomB1bFgF/ErQKJKdwiEF0PVde9TDKSYzN6PyeWfAV9IQbWl5xTnptoyaQDP4HPU\nXjwqCRHni/wZ8PtCB51zNx8dHdlyubTFYmHL5dKWy+Ve7jxeH8ssvWjoB6xH/Nk4OjpKqGK9XSeP\nStLueowWdYcDvSdpMmJOnNqt6np4eLDNZmPr9To8Uqg6hLPE5zTMtMOGUfOjiIiIiJ+BxmF65pCv\nYm2tszrT1AuqhKBOAwmxXq+3SAh9LtTePFeatdNqtbJ6vR7mJi4Wi2BlPBqNQq4Tz8SIQ0ckISIi\nfgD5fD4w2KqAgISo1+tWrVZD0sDQaN+xxCWy2WzC0o5Nndngu+AoxJGQoJ6AkOCiK5fLVqvVrF6v\nW7PZtJOTE3t+frb7+3tbrVZ7+z+MeB9QiWq5XE6oblA5qDRV9zwkRJxN8mfAF40prBSLxbB3UD9A\nQpydnW1Z2KmXarFYtGKxGAoueLFGvH8o6Y6FIfcy3WonJyc2HA7D4tzZx+vUQYQoNFSloY+RnI0w\nS5L8LEhb7cyEaINs4+vFYmFHR0dhb0VkH8T5WiyjwUOXDj+FgJjP5zabzYJFoZKZWT5PyF10n2NJ\nQp4Sz8SIiIhfhcaMEPlKMGBdrbM7c7lcyDX0e8ySDaNKQrC80oIZorrUdpvFTIjlchkeu92u5XI5\ne3x8TNzv8VyMOGREEiIi4jtBV1K5XLZms2ntdtvOz8+3lBD43iuzToGBjkeKZgTay+UyFEl0pXkK\nViqV0BlE15CZhe97fn7eUkJMp9NAQCyXy9CRGRHxs9ilhKCADAFHoERX2/39fSQg/iBoIK5DzL0S\nApkzKgds5r5FQjC0LZfLRRLiDwF7iUSyUChYo9GwZrNpzWbTGo2GnZ6eWrFYDIP+ptPpb3+dSkLQ\nOEBh2BcSudNjwTjC7Ov9ClFbLpdDQwl7vNFo2HQ6tclkkliQXVjXRRwOuPO4K0ulkjUaDWu329Zu\nt63VaoU4irVcLm00GgWl82q12iJcsxprKQkBuca5aWYhD4qIiIj4WfhYjPOmVqsllir5edS8AwWi\nd6x4fHxMEBDr9drMvirbtImUmJVmPa+Ivb+/T6ja1ut1goAYDoepThkREYeGSEJERHwnsGOqVCrW\narXs8vLSLi8vrdPpWKfTCSSE2ogoMaBep1jTrFYrm8/nNp/PbbVabUmqKbbwXKenp6FYgQUFRARd\n58fHx6G7uFarhe4omPrZbBaLdRG/jJeUEHSTeDsmkuZox/RnQQsrOkMESXKtVgsyZxYklichtIuo\nVCrZcrkMQXo81/4MKAnBPmg0GtbpdOzi4sIuLi6Ckma9Xtt0Ot0b8U73HV13dM2x7u/v49DBiC1g\nx1QoFKxSqYT5ORcXF2GfdzodGwwGiUWBhHgv7qfDgXqIc75xtl1cXNjV1ZV9+PAhFKbIE+bzuZ2c\nnIRC2HQ6DYSEWbaLVGn73Bf24h6OiIj4VfictVarWavVCuRuq9VK5CFKOugjFkrqUpFmiee/h0ZW\nXZzT3l5Pm/awVFwulzYcDsMdr24ZWT7jIyJ2IZIQERE74KV0p6enViqVrFarWbPZtPPz8y0bJpXX\nmVkgDNR2CfIBcoBHiml6kWn3MEkJhQq65HQ2hJIVKjmE3VeLnIiIX8HxUHAi3QAAIABJREFU8XGw\n1dFhwqh/zCwQD+v12haLRWKvo4iIeN/QIgPkAbNzKpVKGFzOOQbp4OdIqJcrAbwSFvy9iPcPzp5i\nsWiVSiXcye12OxRo8/m8jUYjq1QqgZD43aDLl2JipVKxXC4XkkxUEKghsWqK85oimJ2DAuL8/Dzs\n7cvLy/BIcURjO4q38/nccrmcbTYbM8t2MTrCQmOR2oMosUrjEzNuODeOj49tNpslmpKyChoRNEep\n1+uJRdOW7uM4qDp70JhMZx4p1GYwNh9FvAX8nAfNIdTiDkWhNj9BPrDIR5SE8OcV96wutRvmUcH3\n6ewbGqd0Ppj/eWHd3d2FmYtpfy8i4hARSYiIiB3wBEChUAgzFhqNhrVaLWs2m4kuXmTwOlDt/v4+\nDBdC9ZD2NbYiurR7mGIGKgg6h7B4UMWFFvB2rYiIXwFkl/r6EyCZWQieIB8mk4kNh0Mbj8dhv8di\n2/sHxTSVPF9dXVm73bZqtRoKxHTzaBFWZ4fQ1amyagpwkBDxXPszQLGuUqkEGyaUiPV63Wq1WvDy\nPTs729veIOmELMEmShPOp6enYKlD7EAXXcSfC/Y4s8fSVLfVajVhb2j2H6KBwi3WTMwDi0Xc7EFt\nPXK5nFUqlWC3pcRqp9Oxdrtt9Xo9ofp7fHxMFN7MbGvofZbgYwEUtDrrBAsmJSHMbKtjOGI/YM+q\nEhEbTY+Hh4cwq4bH2HwU8avQc5MGUR36zL2nBAX7lEeICFwjyGG9HZPWgVBlp9VTOGtpmtKzl6+V\nINH3ogQeJASv++HhIZHrcN5zBkYiIuJQEUmIiIgdwJOXwx8VhBY9Wq1WCJ4LhYKdnp6GC2S9XoeO\npfF4nFiTySQxQBAVhO8Y8SQERV66zxuNhj08PIRLz8wSBMSuyzIi4lehMyEgIYrFYioJMZ1ObTwe\n22AwsNFoFGah3N/fx+DpnQMbu2q1mpij02q1QtDviyd0y3EO6gwRzjo9m30RJuJ9AxKeO/D8/Nza\n7bY1m81AQhwfH4f5TPvqDlYVEB3taYUafgY4LyMi2OOcm5AQ7PNGo2G1Wi1RgKARZbFY2GQySShl\ntXgb79xsQAtpnBWVSiV83pD1Ou+mXq+Hew7C3iucPeGUpc+b+T2oOs7Pz7dmPa1WqwQBMR6Pw3uN\nnfT7he5Z4joULNVqdeueXa/XNh6PbTQa2fPzc/hsIyJ+FrvOTRpDm81maApVQkHnOnhrVx0UraSD\n5hZeYeHrK2aWyE/SzmGtzZjZ1u9BRpP3UAfaleto42k8FyMODZGEiIjYAS10FYvFwJZ7JQTMOgQB\nRTQC6Ol0ar1ez/r9fngcDoeBoGDh+6ddkrwGiAgdqFSv1229XtvDw0OCdfekg2fdIyJeA3idKwmh\nSghvwzQej4MSApVQVEK8fzDLgWLa1dWVXV5ebikhzL7Oy1E7Bj/EXJUQ2DJFJcSfBVVCYFXDfazF\nEJQQ+yQhaBrgtZbL5UQ3HYrLh4cHm8/nwW844s+EFjM4N1UJwUBq9rnvvkRZMxgMQmMMDSo6mywi\nG/CWr5AQHz9+tL///tuazWawLuTRLGl16e0IdcB91gpThULBms2mffjwwf7++2/766+/EnYnp6en\nNp/PEwREoVBIDKgmx4r4vfDDePP5fCj+QpB6LJfLoP5brVaxUSTiVeDnLHBPXl1d2dXVVbBVgnRg\n6XwHivpaY/GEQ5r1kicg2NPMd8jlcolB0/r1rveiqg3yIM45XDA4H3XeYtZI5oiIH0EkISIidkA7\nvfFzhoRACdFsNhOX2unpaZDHaxDd7/ft5ubGrq+v7ebmxvr9fugGx9c1rcMHJp7LB1uHer0eAnU6\nodSu5FsrIuJX4Yd8MROCIhq2IqqEGA6HoSPKLHsJcsTrAzsmkoQPHz5Yp9NJyJ+9HRP7Qq2Y+P00\nO6aohPizAAmhSgg64LRDnBk1+xparkoI7u1arZbw0SZhXSwWNhqNAokb8WdDlRBKQnBusswsQWY9\nPT3ZcDhM3MdqxxRtbLIFjctPT0+tXC5bq9Wyjx8/2n//939bs9lMqAQKhUKCgFgulzuVEFnE2dmZ\nNRoN+/Dhg/3P//yP/c///M9WJ/BkMknkToVCwTabzZZdY8TvhxIRSkJQ/PVgnocOTI+I+FUoAeDJ\n2//6r/8K8+awXuIu1JU2w0RrJGn1kjTLa/a0J+m8fZy34fSNVdR79O/SiKW5Duc9zxVrOhGHisxl\nO8oI6rAj/cFTlvFbS3/oPbvofVSzHLhF7Aea3KUtHQS42Wzs8fHRptOpDYdD6/V6dnd3Fx7v7u5s\nMBjYeDy22Wxm6/U6DKDebDapySGXnBbdlNn3XcDsbWZRMAR7tVqFgUlRzhzxo/ABF1YnEHPYBSCB\nVV9qrMZQP8QiyPuBPxv9vc0wuH/961/28ePHoIBoNBpWLpcTg8x9IM/zE/R7GyYIWQZ0co6qouyl\nJCDicOHVMPj4onjwHWj7jO00FuU1oSKjgQEl5a7EOOL9w5+hKB04J3XmGLkM550qC9XGQUncmN9k\nC0dHRwkbokKhYOfn5/bx40e7uLiwVqtl9XrdSqVSOBN8jtHv9+3u7s663a4NBoOQV2RFJcAZrUNd\nLy4uQgwAUcZeJT+ZzWYJm1r2t1dFRrwNUPD5pZ3Yp6endnFxEciHq6srOz8/N7NkPSWfz9tkMrFq\ntRoKwuSg0R4u4mfglQoQtOSjzWYzkBDqVKF5yc+Qtp6gANpUp8+TVm/0j2aWsICiic/nLavVaudZ\nGH9+sgO/N18amm62TVzpo8Zvfi/5GE/P00NC5kiI4+PjkJhRZPV+kfl8PpVV9F2T6q3P0sn1m80m\nFMXiZRjhkSa/80stQwDdO91u125ubuzm5iZ0gOtQXr1MdgGWn4sUOTaJaVrx5eHhIexvCIjFYhEK\ndbEQHPGj8J0aDFCEgGA+Ct+L9yv7juQy4n2BBECXSp+xzMHf+uLiwprNptVqtfD9P+LZrxZgpVLJ\nNptNIHL1XONs1UeNB2LgftjQZhWKW3oH8llnwUNcXw/7MZ/Ph1iXRHmfio2I/UJtuzhDseHBVoIO\nSWK89XptR0dHCUtP4j3ORI0xYwE3W8AuDnvXRqNhnU7H/vrrL7u4uLBGoxEGrD49PQXlw9PTUyAf\nyC8gIUajUVBVZwEnJydhYCx5C3MuUEGenJyE98b+1WYtn7vs+zz/E4B6VdU3dJNrZ3mr1bJ2ux1W\no9HYqs0cHx8HMpW8Ve/mb+XAEREKbUgiF6UpicY46iQ666FQKLyocNCZCv5Rv9cXi/V705qe9f5N\naxIwsy2yj+fT+qZv6PN3ejwT9w/iuF2zSFjE+d9yLKFWTaMJ96Tmvb4J5dBqe5kjISgyMLmeQ4WD\nhaA8jQ3SH3I+EP3w7u/vQ6C+XC5T/SXjD3OEYhf5wDKzrT04Go1sMBjY7e2tffnyxT5//myz2cxm\ns5lNp9Ngo8Q+fSkx9L77tVotBPOw+xQ0lITQoH65XG4Vg+Mej/gRcLnSvYsM29uTQX5xQUK4kUDG\nffe+QAKgdzTJJsUH7ERIVFutlpXL5UR35Pd2f+tsnHK5nCjsasKAykwLcpvNJpyRcYjbYUMVirtI\nCFVe7SswJ4nkrtdzEEuptGaCiD8PattVLpcDWaskBGrX+/v78LUSEDx6EoI9F/Ob7AAlRLPZDEOa\nWZAQ5XI53Gca0w8GA+t2u3Z9fW2fP3+2brcbcozlcpmZQkQul7NisZiYoffhwwdrt9uhEQEf9fV6\nHd4DhMp0OrXFYhH2s+8gjngbQJCTc1J7qdVq4dd8ratSqaQWWX3eqsW1ON8j4kehqmjuTWqGnoTg\nz87OzrbmM2jO4POCtGHSfK/PH5R00I517t60xZ8/Pz+/SELwWtNcBfz7iNgf2B9aw2aRH7OYg+jd\nJbzjhFq2a0OxLubsaP3vkJA5EoLLTwcBY/XBqlarqT/gSjaQ8KnlDfYgs9ksBPD4S0aLhgiPb6kg\n+MF/enoKRa7NZpNQQlxfX9s///wTEkO1RvKWEWlQJQRFX5Xo0zXnLyyCPDxjoxIi4legQZ8OR69W\nq+F8brVaNp1OgwqCC3O5XAYbgRgsvS+ghKhUKuF+xveeR13c6QRhKlv1SNsrSsr6Dl+zrx3y7DsC\nNQ3SiB0iDhfenkstvZSIZ3/ss/hKQsqdzN5jL2szQVRC/JmA5CfO404l1js7O0tYbpKzPDw8pBIQ\nFCq82jaSENkBSohGo2FXV1f2999/29XVVWLeHEQ7nyf5q+YXnz59sm63m7AjzEpRN5fLWalUskaj\nEax7zs/PrdVqJZQQOkNvOBwGJYSSEBSs4x5+e6CEKJfLVq/Xg9LZL++5f3Z2tlVkNbNQFOZ71+u1\nmX1VCd7f3+/z7UYcEHwDCoprVULUajUrl8uJ7vN8Pr/VqKx1kDQCQr9Om6vp6y7fU5f0j5AQamXL\na+E17FJCxLMwG1CCiuYibcJD6UhcVyqVEnVEche/5vO5zedzWywW4evJZGLT6dROT08T5Bjx4KE1\n2GWWhICBb7Vadn5+bufn59bpdMIAQv9DrRZLWoD1LBIdRZro8fe1qJyG7+1Q27UBfrTDTZ8nTSZ2\nSBvtvcBfOPf39yEBZA0GA+v1esGO6cuXL1sKne8lAZRV9SSEFi/UI1DnQagawl9eERHfC1VCcMHS\ncULBuV6vh6LI4+OjLZfL0JnHAPW4794XsBCpVqvWbDaD3ZIWUur1eqIrpFgsftfw3V0kBImHL7Ro\nYkKxBq9VLVBD0uo8qIjsQ+MnEjY64XynOE0o3Hf77BbTDjntSqfBgEYCHbDupf4R7x+qhCBZTVNC\nkGxy/mnsyVIiIjadZAvaMakF+n//+9/28ePH0M1bqVSsUCgEZcNqtQoqAebM3dzc2PX1tXW73T2/\nq20QM/IeO52Offz4McQElUrF8vm8HR0d2cPDg61WqzDrIk0JEQvVvwdaSNO4rtPphEe+xnKEu/jk\n5GSr8Pr4+JjoAi6Xy7ZerxOx2KEVziJ+Hbu88H2ty3/N9/qZYJAQav+mtrD5fD6ooc1sK3/wtkqq\nuvIFY21i5vs9waB1Se/IogtVbBoJwet5fn4OZyHNpFkhmv8k+JkgaQurYFWLUcNut9t2fn5ulUol\nMTPCN+TxOJ1OtxZkL3Vs3xDtZx37x6yds5kjIeisrNVq1m637erqKlg5aFHD/7DrAZBGTLCQe7IW\ni8UWeZH2w60d8TymsaV+7dqo+rzAy7v8MBL/fjjA4rDNtwEzRJbLZfjce72e5fN5MzN7eHiwcrm8\n5cl7d3dn19fXNhgMbD6fp3bsvgTdE3RKEgx2Oh1rNBpbyamZbe2BtAMnawdQxGGAS1H9+NMKIySS\no9EoJMrj8Tics/GMOmx4+SiKxUajYefn53Z5eZlQPtCNpAN39X59yWuVr/XuVRLCB1pYQ5VKpS3J\n6nw+t+l0apPJxCaTiZ2engZizFsHRGQLFLN0MbRX5yOZWUKVSIyHDeE+775vqSr9ILtcLpdqGxDx\nPqEzIWjAYo9z11Ls8zkH9+58PrfxeGzD4dCm06ktl8u97/uIr4Ak52ccwsnbqxLPo4CYzWY2HA6t\n2+1at9u129tbu76+tn6/b/P5PFPFeV9UUUIFSx8smB4fH221WtnDw4ONx+NE89bd3Z2NRiObz+cx\nbvwN8HcQZ1Cj0bB2ux2Gidfr9aDMUnUpNQoId69MRVVBYymFVzML55dZzE/fM7R+tss7X4v5fgAz\ne4w95ZUQ5Bhe8ay22Z4c0Od+Sc1gtl3I9bnDLpLhe+yYvkcV/vnzZ+v1ejabzTJ15r9XpMXsakdN\nA5SqWHK5XOLOUyWEzn7ySgjNA1QVoWSbzuXRAew0eqr6X2cgspQMy9KdmkkSolgsBvbow4cP1mw2\nE3KWarW6RULsmg/hZVFq0eC7hugc12IEB49PEpWE8ESALt3AaVPR09hfPUD1IKOYMpvNgjQrDqd5\nO0BCaActBAR7iU4llDbL5TLYMSkJoRfdS5+R3xtYMUFCoATyySl7Jq2DUvep/7OIiO+BKiEgISgu\n4+mLKmg2mwUSotfrJZLJuPcOF0oEsJSEIFkl+OIRywVNBtgHuzpLVKmopD8BGWcpdysFPAhbVT8u\nl8tg81AsFu309NSOjo7CnmRFEiKbQDHAoMyzs7Ng9aXWhL7zbDKZhJk0+yzGfo+tox+0mMvlErMs\nIhHx/gGJigWKkhBK5GqzyfPz89a9qyRElpLNPx3auYva389Q0qYiirNKQnz+/Nk+ffpkg8HABoNB\npgpSWjBheS/sarUarMVQy5pZmKMHCdHr9cL8vBg3vj20qEshjSLX+fm5XVxcWKvVSniae8UeBV/N\nc709CSQEMSB7nL8H4uf9/uALrTRY6vw49oNaDGqtj31BzE8+uouE4Gz05IZaY6bVDX3BNm3u7C7S\nwTc0+wXRoVahabNGFc/Pz9bv963f79t0Os3Mmf+eofkuOQh3NXu2WCwGdwjUNihyVJnj70GGpPum\nPq+GUKJNFT7k3NSB9edFa9pqA69NeVmaq5Q5EgJfVCUh6PzW7kqvgNBCvCcElO3UD0U/KP19b1eD\nZ5suTQb8UBo9bJS15fBNK7Z4BQVdMLrG47GNRqMQpHIgImeNierrgsIqnwey0fv7e1ssFjYej+30\n9DR1cAxE0Xw+3yKJvvUZ6eEECaHWZMj06UihYOG9zv2eioj4WeiF6EkIgj6IOU9CjMfjMIw9Kxdf\nxM+BfcBeoOivSggdBoddnCarejea2VaBlu9LUw1ql3hakEZcoMOo8ZoulUphv/LvUwSBwI3nZPag\n9yAJADGhdhFTsFqv1+F+VhJin2fPtwgIr4LI5XIJH/+I9w1VQlQqldA1pzMhKLKwL3whbzabhRxB\nlRDxzs0GdN4h5D1LlRBqF/L4+GjT6TQMov706ZP97//+b8gtsqSE8F2bEGpaOKlWq6GhQNX9XgnR\n7/cTeXk8A98WSvTn8/lwBjWbzdBc0mg0gr9+mhLCzLbuLK+EqNfrtlwuQ2Mo51ZaQ2bE+4LG72rj\nql3i9/f3Np1ObTab2XQ6taOjoxDTcWaYfT8JYfY15zg+Pk61SNImZZ0l62fKpi39PiUfWF5lkfZr\n/m/88j8H6uCSlTP/PcPXbs/OzoJVJkvn3PDIftTlc2Ly0LQGJSUi1P7a16u1+dnPnNUZEqzxeBzI\n3izZG2aOhEhTQuAfSTCjXW8cHmmBdpofVhpr6ZkkX8g1s0R3B969nuBIU19wuWvhxls7ceAogaKv\ni9Xr9UIBhj/nNcZE4/WhSR4/4EpA9Ho9Oz4+3iKy0mR436tC8AeTV0J0Op3QBapKCC7ZlwK5SEZE\n/Cy4kAn6lADTYbDakdnv963X64WO5NiVedjwlkg6qwYlxOXl5ZbE+uTkJBGAkxzsCrrZT56kUAm/\ndo5jzaT3sVdKLhaLQIZg2aPFvKwEZBHbIIZSGTJ3oNoxQXJiYTIej4Md0z7n0XyPEkLVRexrs6+x\nXSTI3j9UCcEeJ8nVxNUsmdNEO6bDgBboUUF4JUShUEjkkY+Pj1tKiP/7v//bKpxlBb5wotYUEBE0\nA6LgWSwWQcHDwO3BYLDV0BfxNlDyiJhO7ZhQQtRqtUQTJo2YZl+Vemlxm1dCUKzlzMrn8yHmA/HM\nen/QmJ1zsFqthubKTqdj6/XahsNhiNW1OYg4nRkNOqMwjYTw8xogNF5SLuyaJestt30BmNqPJym8\nA0Xa1wptuvLwlvMRb4s0C+BKpZLYrzSK6P2mtWKdl+Ptm/g3eOSsVBLi/v7eisVi4r5Pm33sCTNs\nhyeTiY3HY5tMJsECcb1ep6pt9oXMkRBmya4xHSKtHem+yEui9q2EzxdDCOD1YPFF4+fn54SMlgKL\nSqu+h4Tg8XuUEJAMahvFpa8qidVqFQ7dOIDudaHqFjMLhavNZmOLxcKm02n4fVWs/GwApYcQSwN5\nlEDIubDC4bV6mZ+fjaL+ijHIi/gRcI6RQFMcQZZtZls/G6PRyEajUTjDYkHksEG3Lp0dNAuo12Wz\n2dySlBJoeaWil6PuUm2lJad6d6Z5qPqzEJ9MTTS0e2+9XsdCb8bAvvE2NQw+x6qGe5DPkaJdv9/f\nOwGqyYUmI1rMSYtTfSwb8f7gm02wG4NsUwKCxifOKCVmtaA3mUxsNBoFn+CohMgOKNDz+XqSCcJe\niwur1SqoW7Bg6vf74TmzdF8pWYxVXqvVSqjW6OiEhFgul4mCCWs2m+377fxR8AQZ5w85J3vV22f6\n5kmN68y+dqxzf1NbIVfwxbM0NwnfNR5xeEjLHer1ehjUe3FxYRcXF7ZcLhONQljHUEvTWMi7naCA\nZbg9jRz6d75FQLAfPcmQZuPu/9wXhzebzW//f474fnyrXgzBxWO5XLaLiwu7vLwMj81mM6Hyq1ar\noWatefC34nmU/Up8EO/x9a4ac9p+VoUG7wESD2cAzuJ9zxTOHAlBwWA8Htvd3V1gmZAB4sHlPwgN\nzPkgPQNFsqoLNpW/n8vlwvPpB8T37VJCeEUEj7qhKCybpRdWzL4emOoJRheBFpYhHJBnQVxEvC28\n1yD2TK9BAGkXEUsDeA4U9hAX9dHRUSprrzNEGMLOMNYY0EX8CNRKAFWOqnFUDcFFR9BGwult7iIO\nCwzTVGvEy8tLOz8/Dx0hDAgGSo5qA4DZdheITzB0+cFxSjKYWWoBV+/Q5+dnq9VqgYxAVUHxerFY\n/Kb/xYhvwZNTdCCRtDIkk66j5+fnYAM3HA6t1+vZ9fW13d7e2mAwsMlkYsvlcm93nnZSqWyb5IDY\nULvcf8S+MeLw4PMCHTqInF8bniCtPAGhTUnz+dym06lNJpMQ60XiPztQH3S6zMltaTCjMI8lyWw2\nC0OaF4tFIsfL2ud6dHQUioudTidRXGw0GlYul+309DQUm9VCLCu2eX8ivFoB8kFzTrrMzbZjM39P\n+aKe2syZWbjrdDh7o9FI7S73nb9xbtdh4ujoyAqFQsLKptFoWKvVCqvRaFgul7PFYpHYcxrTmyXn\nIEE0sKc4VyaTSaI5Se9KVUmnPaZZLnmPfa964K7VRs+IbCLN+sgTDmkWSpVKxdrtdlitVivUQFjF\nYjEQsLpfPbGqM7149I3uDGpPy3mVoOUsNftK+uq8Ed6v7s+HhwfL5XJbRNw+YorMkRAPDw+2XC5t\nNBpZt9sNF6MWZlEh+AnzPnmlaOYJDP2wlS2FaNBBwqw0SU1accVfyloMSVNj6IBNLcaQdJydnSWk\nuZ6EIHhVe6aItwPSPrOvB8hrXTzsWdQP6n2N5QTdUly4JJleQqjMPUQEl2cM9CN+FLo3PQnBnlSC\nTvegJhBZS5wjvh/Hx8dbEurLy0trt9uhU65QKOwk5fV+9EjrENFOJ30uvfuRa6uFjaoNee7j42Or\nVqsh0OK+Jd4Yj8ex4zwjSEsQSqVSICG0A4kGErWBu7u7s5ubG7u5uQlFWWZ9/W6o1QWzdLTzmVhS\nCTTd999r4xhxWKBrnKVJrHoKs0c051ApPfGodoFOp9NQLNmnDVlEEt6WhsJ8oVAIBBPWgShaIFXH\n43GCQM8iiA8gIf766y+7uLgIyjVUa2Zf1Yeod7DNy/L7e8+g9qGzSsg5sV3FVtNsmyj3SCMh+He4\n+1Td2G63E7MAtHFusViEfDeSEIcJSIh6vR66yGkkwVqzWq3a0dGRTafThNODby5SC0IzC7Uwr4RV\nol5JCL+8e4TvMvfd5t4KPu254hmWbXibfNSJqMB0ADVWiZCl9Xo9qP99bfrs7CzROMciRoO00kZ3\n9orOi+Kc8/mvrxubWaLGrE31Wn8+OTlJ7E+URdRozGxvd2/mSAhVQjCki0tLiQMv26OwoCRE2sYi\n2Ic5UsnLS4wTiSRkhPpce4ubtA5PXX4z6GtnM5kl7St0M+sgbvyvF4tFpny+3is0CEs7KH7lh9h3\nm/vhhJAQFNtQQuCx6ofW6HAahhmpfVlExPdCrQTSSAgdNqjDYZHTxgTi8EERpVqtWrvdtqurq9CV\nrl2dGrTvIurV+sgTBmZfi656F3P/+cXd7G0A9D7N5XLBjxr7k+fn5xBr6PDsiP3CS5J9seLy8tKq\n1Woo0HL/YQHX6/Xs5ubGrq+vE/fivuyYVAnhC83qEat7P63DNOL9QFXO7AvdG0pSeR9hJasoXGMh\nAenmm5Ui9g9VQjCQtVKpJNTNqoQYDAZ2d3cXSIgsDaFOAzk3hcZ//etf1ul0grUPSggKyjq7Jyoh\n9oddSgjNOVUJQb1lV76bZnEC0cZdztwJZolRONalFnTslyzv/4jd8CTEv//9b+t0Ooku8lKpZM/P\nz4GY/ZYSwuwrmUnOyT5CdeNV1L6h1zdIpXWe+3qg72j3+U20Dss2tGmNs80PnMbu1Vst6V3G3Q2R\nwfKElm8UQaXqY/xCoZBo1qTG7JuSvDuANt/7XJrc4/T0dCtvNrPQ/MDMlH0gcySEdiYiIdGOMQ4m\nf0hQyFfVAYlrrVZL2NBQ7FWWyCxZ+PAHFZtWvXx9h6ZZ0hZiFymgbCqbSaXZdMal+S/6QxQFBINH\nIt4W7DU6cM22B6D/LLTrs1arWbPZDPuXi1mLZex9ktBdlkx0l6R1WEZEfA/SlBB+LoRXQnDhxmLa\n+4BXQlxeXm4pIYrFYij4qv+vBlN+H+zyyvRJwa4OJT+smq/1vjazcPfTefz09GTj8dh6vZ7l8/nf\n8D8Y8T3QuGwXCVEsFhP3LnZMSkJ8+fJlq4ljH1CbReyYPAmh8WJa0hHxvqBdaxTk6IKj+1iVEHqO\n6TmZpoSYTCaRxMog0pQQEPdaDCCf6/f7dnt7m1BCZNly1yshICFoHuS8oxFP55jM5/M4w2SP4Cwi\nvt+lhCCe80Uw4AkIyHX2t9l/7rdarbY1i7PX61m32w2KGc1zV6tVrG8cMJSE6HQ69u9//9suLy8T\nNji4kBAfqRLCkxDUwJjlRp3Dd6Snqag9AcFz7nr8nq/THiOyCW+9VeOVAAAgAElEQVTXzxycRqNh\nnU4nLBQPLOyGaRJhj/KcLGq7Zl/rhdx3s9ksKLO9mqxYLIb9CHmwqyFJ95jmS36wNee6jjDwah3q\nNZGE+P+gu3s+nwe/ZogHnauQxjqqCuL4+NhKpVKiCDuZTMIFi/yrVCptKRV80YPZDkoScCH7zeFJ\niDTmyltJmVlISpEtcgBrMq5esdod5Q/piLfFa5IOusrlcuiQarfb1ul0QoFPuwN8QY/uKZWyDofD\nENyrLD9elBHfA6/m0k5eVDoQEGrH5AvHUf1wuNC7lCRCO+V0DgTkqAZVXvlg9nWItLcphGTXBFdV\nNd53VVVdDMNU/0719zT7SlCQaJ+cnNhsNktIaxuNRrDtid6u+wFnDV3hxWLRzs/Prd1uB1K+Wq3a\nycnJln90v9+30Whk0+k03Hv7fi+QXnp2KnmblmjHOO79ww+C9UW/NIWM5iWbzSZRwEPxw7kYsV94\nEjyXy235nxPXoyT1w1XH47H1+30bDofBYitLn60WPXK5XBhgrPYq5NdmFvYmVjtYTjFIPSohfh+8\n9TNEPw1G6nfO/jT7qvonBtM8kjiPmg1xoB/MqmSCKqx9zUXVrzQ1sT8iwZptePvyYrFoFxcXiViu\nVquFuMfMQjOlb6jEvkYLp/6z97Y37D9fJ9R9FW2T/gz4e1iHo5NnNJtNOz8/TyzuMB7L5XJ4Dmz1\n1+v1VkO8z0tQNqrdHCSE5sqVSmXLRtPXjoFXO0A0+FnHZl/vaeJDJSF4D4vFIth9/u4aYSZJCJgZ\nPmg/bZzivhZjzZIDDY+OjoIVzXg8DnIvPL6Q1BQKhVTiII2E8ExTmvzF/752gmpHqB6iJycnCckP\nl7kWatSmief0A0rigXo48FIpgngICO+3rsmK2VdJLIU6fGSHw2HoBtXuKR8sRkS8BC1AHx8fJ7p4\nKUKrFJEkmr8bcfjw6j/vk0nRjK41GgjSBrR5QsM/0h1JFwnemahpsJYj6FMSnwTavy5IE5Ji1Gu8\n3nK5HM7cVqtlnU4nzM7RBCji9+Ho6ChYfkEOMeAU9dXZ2Vm492guYRYEd94+i3UaE6YVm6vVakIJ\nsYuA8BZlEe8Har2pg2A9OeUJCFW90mBFjBetl7KDk5OTRKGjVCoF+8Lz8/NARDCfUC1n+Fw519Su\nKEskBOcaS20sKF7zvrQDWS13ICFQQmAvG/F22BXbY0miQ1eJ78ySJCjnUFojJtYfmtv6+E+bNvlz\nFGG1Wi3RZILVHHZkabWPiGwhn88HKxvut3/961+JeV4675R4fjwe23g8tslkEoq2SrC/dDaoI4nZ\nthWT7zyPeP8gn9B7mBqG1oK1Ea3ZbIZ5D5AU1N4o2tPgpIPMaZZTG3QWriQ6J9Msab1arVZtuVwm\nXHvSzlclc8lnUVFAKusjebyfzekbmHUQ9i7XgrdAZkkIihr39/dbw2m8woDlFQ10oGlXpHZM0nWk\n3Rx09Ho7pjRCwD9SINbk0fuD8Vw+CGi1WqFYrIU93Uh6gatlVLyIDw8cEuzLQqGwVRCj8AIJobY3\nZl8vWfWRHQ6Hdnd3Z/1+P5WEiPsk4ltQKTUrn8+HQhpBJcVcVlQ9vC9QQNUhgn6hztIiinqRczer\nklAJdYWeZQRrk8kkJCSTycSWy+VWN5MOU6xWq7ZarcI+1aSF10FyzD5uNpvWbrdtMplYPp+32Wxm\nZl8DzIjfByUh2u22XVxc2MXFhXU6nYR9CffabDYLNg53d3c2Go32SkL4GNQPpWaP8nPlh1JHNcSf\nAe0ApvCGnR0kBB3EGu+T7KrVpk9cI/YPhl1S3KjX68G+EBKiXq8nft614MqsBBTNFD6yFGPxHik0\navEaMg1LZSX2lYDgkfcXlRBvD2/foTZM9XrdWq2WtdvtQIpqk4mSEBBG3joQ6w+N0bRGouQqX5+c\nnIT8YrPZhL+rBESpVArnnNqdRGQP+XzearWatdvt0FT54cOHUNOgqZL9RMw/Go0SMf90Og177aX7\nTdXT2pjsa4RRRfNn4ejoyPL5fDjb1FoJhYNfkGdq7YXbgx9OrnMeiMnIVXUPe0W/EgFm/9mnjUYj\nEAUQCt/jtnN2dpYgIPyoAXINCAie4+npKdQNIZupvVOn/qNJCFVE+ORM/3P00XeOeXIBT04dck0i\n6H1Xv0UcUMzQTtG0wop2d1Kc0X8P6wG6Cvi1+oQqCcF7VWVFJCEOE+xHWFotiHU6nTCAk4IfJIRe\nsHTHUbDDRxYSgu6RuD8ifgScdyph9HY8qkjTjveIw4cvoNIppx0kKA50eCDJgldCQGpBAKQFWNqZ\nQSA3GAxC0WIwGNhisdiSWJdKpVDooZtSixnaLKDxgCoh2u22zefzUPjDAiDi98KTEB8+fLDLy8tg\nY0IHHZ/zZDKxXq9nnz59Cl21y+UyE0oI4kQlcEl0dD/uIiAiGfF+Qf6QpoTQoZyqhCCXQOI/n8+D\nEoICbozzsgGUEPV63c7Pz0NTEYRqu922RqOxNSxSFS4oIabTaSIfzQrIVWu1mrVaLTs/Pw82K2rj\ng6e/WsUOBoNwt49Go0TTXywsvy24l7RA5ZUQ7XY7FOC8EkLVqmn2NqhTtaPWx2BpSthisZgolJlZ\norBXLBYD4crz/q5CWcSPgRiu0+nYX3/9ZR8+fAhkBHFcLpcL6i7m4KgSgiKut1FKgxIP7At+3z/G\n/fLngHyiUqlYq9Wyi4uL0ABAzojqQWdykdf6uYKqdlALdPLV6XSaINdpIlCLubRZJGZmk8kknH/k\n32mEmT9HsTyEgMA9yDfHay6O9dJ8PrfRaBRIiFwut2Xd/tbIHAlBIQKm+7XhNxZEAHMW8NFKIyG8\njYQnM9LmRGj3ErIdpPisWq0WOggYwMhAbk9CqMWTFnoiEXFY0ENDE1GUENgxEcyzV05OTkIior7p\nXOLD4dC63W7wkc36MLuI7MErITRRoWOqVquZmSXI1Vgwe19QP3s6dtOUEBTLsGPSQIuEQEmNs7Oz\nRGedEgqcZQRz/X7f7u7uwprNZltzJ6rVqi0Wi2CfpEGeEh/acIBcnDOXjmKKfcvlMgSeEb8XnoT4\n8OFDolMJC03smHq9nn358iVIn/dtx2RmW3seEoJi867vj0qIPwOqLuQc8jMhiPm1gzMqIQ4DSkJQ\niMMPXZUQzPQgn/N2TMPh0BaLxb7fTiog0er1emicarVaCTsmMwsKSSUg1IppNBrt+Z38WdAGI43t\ndR5hq9VKqCW8EoJaRtoMB1+T0KIYcZvOu+Fr5kKYfSVK5vO5TafTxNmoyrCIbILuc86+v//+22q1\nWliVSiXUth4eHmyxWAQCwtsxfS+oi0VEKJSEQI3I3Jtms2nNZjMU4bUW7BuCKM5zR9MkoPaCw+HQ\n+v1+Yg2Hw+96nfP5PPxMUBtMc/vxjfWPj4/h+0ulUkIVwRnP2avzIh4fH200GoWcSr9PSb23xh+X\nZauKgF+zdJCqslZKQigZoRcyHq76nGa7lRB0iVCUUW9NP+gT5p9NPxgMggXBcDgMPtYxATkcqJcb\nw8DoIvKyfFXZ6BwIJIwwsbrwkI3y5ogfBReY2teRWOplRbKhskQdfhRJ0cOBJ9lzuVzClx9vfmxx\nyuVyolDmAza1JiR4Yq9wB5PIsgjotEtSrRsgVX1gRmDlC3bahU7hOpfLhb2JlLVSqViz2bTNZpPo\nYKeArPd39Kx+XfD/TFBdKBQSsul6vR4smOjeYSgvHeF4sKKOIFnYB9hvxHQMvKvVaoGwM9uOLyHR\n9PyM/sXvF5xFSvAS83GWebWYmYUuPKw3VfGaNbuePxnayOGHtGqXotptefI+Sz/3+j54VHUHC8s8\ntWJaLpc2Ho+t1+vZ7e1tsM1DpR3xe6GFLOoPNGgo6UB9g/NH7TEpFHvbpefn59BVrN3F2nhZKBS2\nXg+xoy+mNRoNWy6XIW4sFArh3z46Oor5bQaAo4Ouv/76yz5+/Bjs5xqNRmK+CDE/7g3dbtdubm6s\n2+3aYDCw2WwWmycjfhh61xKLX11d2YcPH8Lj+fl5aKSELCcu94102uxNjqprPB4nVBCcjcwy+ZH7\njXtf/91dNurUAakhkzdQY/bzk8l3lYTwowogX3gtv6sR6o8lIfhaWR+Vu+qG0G5O9TKEYPA+17px\n/EZ+fHy0s7MzM0t2Q/mlUlZUFHTGcGh3u91QdKabJuIwoHJ85Mx0R0FCaGCoCYt6x3opGI+QEByo\nERHfCwgytV/yJARnE/JsCoK657KUREe8jDSf4Gq1GubTdDqdBAmBVzAJq1oRKpmhe0Dl+ljIqZ8m\n5IN2kNANxdJhiGZf71qCLfajkrzlctnu7++D1PT09DTc6bxP5uYoAUGwRnf9fD5/URIe8eNQi8yz\ns7OEtVa9Xg++rdpJCQkBCY8Cgi7xfXaEo2bldZ+fnwcSolgsJmaOYRu22WwCieJJ3EhEvE9oA1Kp\nVEpVQSgJwT7ArgcSotfr2Wg0CmdjjPWygV0khLdJUNsQzROz9jMPQazDtpV8oNhI/gIJ8fj4GEiI\nu7s7+/z5c7jbIwmxHygJ4QtQ2vBGPYTiFgVjXdy1qnplHpdf/L7ue16PKgdRyh4dHSWGsedyuUBm\nQHD9SKd8xNvg9PQ0xDysDx8+2F9//RXOhWazmVD2QaaPx+NgIa1nQyQhIn4GWASWy+Xg2oCaGiKi\n3W4nbJeotZpZQl3FnCLWYrEIOSqLxjhd5CM/U5PVhoRdSi+a+linp6eJZj5ICNbT01N4f/7c9+MJ\nTk9PE7HI70AkIeRD52LT7/PBoBIRu4Yt8ffNLHFBU8AolUrh70NC+IHZyGVg4J6enmw8HicsKrrd\nbij8xQTksMDhwUGJEoIh1OwBCr5KcFHAwJNOB+BQrCPAJ6mJiPheqBJCfcyZS6Kqr11KiH12I0f8\nONQ6hmRPfTQ/fvxonU4nFIdVCeFnMfm5TWbbpP5qtdoiTvv9vvV6vfA4GAwSAy1JSPV59Z7mXFyt\nVgmVGXuSYpBXQpTLZXt6egrvh0CN7x+NRkH586PdLREvQwdjQsgrAUFRS+MpJSE08Fdv9X2SEChr\nGKxNcQ6fWX+Hr1arBAnhC5HxHH1/2KWE0JjPbHv+G8UbVNG9Xi8qITIK33GuFsBpSog0K5us/OxD\nQmAdVqvVEgTE5eWltdttK5VKiXkQWBtCQnz58iV0ii4Wi3iX7gF+HoRXQnD2EO9w1w4GA7u5ubHb\n21u7ubmxm5ubxD0FCaF3N4/UMFCnEjdC1nFfM1+MnxsUENptjwJiNpulzuGM+L3I5XJWLpdDw9L5\n+bldXV3Z1dVVQgmhihkaLzwJgUIq2khH/AxyuZyVSqWg4G82mwkVxIcPH6zRaGwpE81sa24DZAL7\nkflzmqPS/KEkgBIXPxKP+TgAEsDn1/7voISAgCDX9bEEZy1NeF4FQexJLTwqId4IBHVYLOlAVSUY\ndslg+F7/d3Z9YN46ggTUbFsJoUREPp8PxRM2tCohICF0oFdMQA4HdOFSeMGjTu2YCNbU7gTFDkoI\nHYyjyw9wjYj4XmiBhMGDaXZM2nmuSgi1E4k4DPjufyy4ms2mXVxc2F9//WWdTid0QZZKpYRtiELV\ngkC7Kx4eHmw+nycGT2Mx2Ov17O7uLpAQXkWoe0rvct2Hi8UiMWiRPXl6epro3IOEwKO4VColCAj1\n08QWcTqd/oZP488BMRCEkScguA/VDovP2dsxaRC/TzumcrlszWbTLi8vg/zbKyFQuHKHq50UFhMv\nxaARh420eTtqx6RKCJXoQ/brzJzJZBJJiIxBbf24U1UJocVeM0t8xmrFlhVAQhAT0Jygq9VqJYo7\nqoSggMPsHoo1kYT4/UibCeGVOmYW8sz5fG6z2SwUiz99+mT//POPffr0KaF4prGDhjqIJk9A0MCi\nJARxJD8bZhZsm7Qp5uzszDabTXg9kYTYP5SEuLq6sn/9619BPd3pdKzdboc4nO7w+/v7kAMoCTGd\nTkMhNc78iPhR0Nhbr9ft/PzcLi4uEiqIq6srq9frZpas47LXIMiYN6fWc8xb1bVYLLbm4fj1I/BK\nCF9fpgao9WQICLVjwvZfz2cldyEh0pQQauf0O/DHkRBmLxMMbw1VUdABWKlUEv6JJCIEaSTZOrgn\nDvQ6LPih5mp1AwlRr9dDsVe9gbXbl+4ULnB81DkoCfzW6/W+33LEgULtmFQJofuSAomqICC/6ObN\nUhId8TJ8YopCgLMJWxkCFTrnOJ/S4LvXtUOE5EO7SvgaEuJ77zclISjs0glDcXe1WoX3BhFBIZDk\nmMYEtWRSBcR0Og0JcsTrQOMgBmPq8EKGn0MCaTyEjRdK0H2CREGVEJ1OJwzBq1arwUYC9dhqtbLZ\nbBZ8ZZkLEX2u3yc0kdRzFmIXqxGvMCMp1buWfTMYDMLPQSQhsgW9S7hP9NdafNW9QZc6ewN4BaDm\nsb+S06Y11emvzSyRpzCEmpig3W6HQqM+J2c2XaTYh61Wq9ggtUd4hQ4x3S7bX3JNPr/b21u7vr62\nT58+bX1+x8fHIcZTQh0lQ7lcts1mE2Ze8nPB9+jPCa9Dm6Kenp5sMBgEVXYkIX4/fPPt2dlZICcv\nLy/tr7/+sna7nZhzWSwWQ9zNkPrxeBwakMgBFovFvt9exAHB31WcMcwwvLy8tIuLi3Bftdttq1Qq\nW0SBxuTYvOqMQh673W6Ya4QTzVvA3+P+blY3H/07u2KBtOfzz50Wj/wO/JEkxO9G2qHNDwoBHYkq\nncawcxQ/GILys15jEfuFerDRUaR+iRRd1HOfIq/aTKzX63AA3t7e2u3trXW7Xev1ejaZTEKAHxHx\ns1A7JoqDqHN0b2InQke7+lPHM+qw4D2sfcemBidp9ksKr86j60lnK5DUpg2f/pm9o4U6MwvBJEqx\nSqViZskOZIZw8XuoIXw3OgmTEnBmsUP9NUBxAsWVDjbFuojkgH2DH/50Og1DK/cBDd4p7GhzgQ7W\nZr8R1zHPSRVA4/HYlstl7AB8Z9CzlfNUm450eKt2pNFJbmbhbIPMVSsytWONZ1J2oB2NxPHeGkGV\nhw8PD1atVq3dbttisQjnwGq1SlXT+3uW59bifppS3xcpuOv1ztevT05OwnwoCovMsKvVaqEgfHJy\nknivDw8PiSGdzArLmtXUnwY/g0GtkbQTV20Pid1oBHgpxyRPXSwWgeigmUntWjkTX3KbMEvaZsc5\nSfsFDWo656bVaoU5XjSQMDPOzMJwevJE1s3Njd3d3dlkMrHlchnrFhE/BM1XeURBjRVTq9UKDZQQ\nn+rgwKKJUhc5KWQEj6hOX2O/+rNYiWHuVN/AoHh+fg5z9LiLaWT35DLNgEqykE/tUmP/DkQS4o3h\nE1W64MvlstXr9S0SgkIfwZwmqwR0dBpHHA50YCWdkp6E4ADRQi9EFN7oi8Ui0Y3y5cuXUMCYTCZh\ncGtExM/CkxBKkOH1S+cAXW6DwcDu7u7ikMwDhVcAqD+wJqm7iAhAkqjKBxIQlbbyyNcQ7JDsP3K/\naeLMr0mC5/O5TadTK5fLCQLi/v4+vC8drq0zBXhvo9HIBoNBIiiMVjmvA6+EoGhfKBQSthCqGoC0\ngoTYVyzki8vYRqjCkXu9UCgEAov3w7l5e3sbbHUiCfH+wH2qswE8+YAKArKKvaKFQYp7akXG7C/8\ngGMhJxugSJpGQgBPQnBvtlotu7+/DwU/SAhVD+j9StHAd3fSRa75J69N7Z68JYMSYXxNvqrDZ/k1\nCtmTk5PwWih0kLNCFvsCcrw/9wOv0tEZNLpnid8gIdRuNQ1eQWH2H5KLu93btfq96F8jz+mJiEhC\n7AfcZWod3mw2rdFoJIgI6hhm/4nfyBP7/X6igfLu7s7G43Fsnoz4YXA/6p2lJASkOfuR2oWSENyj\ns9lsi2yg8ZtH7jLqG7+6X31tWIk9YkG1BlabTv17xWIx/Nz52WLEkkpC6EwxVNh6vv/uWDKSEL8B\nvusAJQQkBIVouo1zuVwohkBCYLkTvV8PE6enp4F8aLVawa/OkxAkBHSIaEBHJ6iSEJ8/f7Z+vx+C\n/niZR/wqSI4pqO1SQniSFBLiNTsFIn4ftBvDD9DUIoYnIzQZPDo6Cl10dOrOZrNEB5QS6rq0qPIz\nJISSEVqkg4RQiwtmRBCg8X5Jjvm/OD4+tn6/v1VI9v9+xM9hlxKC/2uzJAnBMN7BYBBUM/ss2vsh\n5npmooQgYdf3o+dmt9uNJMQ7hrfYYQaKFnIgIbQATMd8WtJMQwqexDrMPGL/8EMmISH4jPSuzefz\nIVaCbDL76m9NLKXWEVhwqSUdOaP6qXs7qLTCr+5DT4yxSqWSlcvlxKpUKsFKGBIChSyFDixi6bDM\n4ryLPw3efkOt35TwVNWVV0K8VH8gZyUWPDk5sUajkbBqxbP8W3tByaosDmz/00Dhl5lr1DQgICiG\nUvA1s0A+atPF58+f7fb21gaDQXRwiPgpqE0b91aaEgKiPI2E0LlszNjCDljt71EMqKXwa5AQfh4O\n8QCNCTpjyc+UYjEvh0WdxhMXaSREmhIibf7iWyKSEG+MtAtfSYjz83PrdDpbhT4ua7pI+/1+JCEO\nGOoVfXV1ZRcXF4GAarVaVq/Xw7BXCmNmlvCOxsLk7u7Obm9v7cuXL/bp06cwwJVEJV7mEb8CAk2d\nC4BKS5UQeqFFJcRhY5cSQv15v1cNAQlBoXU0GgULOR6xntFiCmfXj3pFE1SS9B4dHYUiHSQEQWix\nWAx3K/8GBcJ8Ph/eDx36uVzO6vV6IIg9CRGT4V9D2kwIJSF0DpKSEFmxY+Lnhf3DkGFIiEajkeiA\nZ49is9nv9+3m5iYkBIvFIg5rfWfwJIQv+OosOJXRM7cmjYSg6YSB7LE7OHt4yY7J7KsNIPeQEv1Y\n2DQajUDKK9lE4YClihgen5+ftwoWer/y71K8gFiAZIB4wDoMooxHJS/Ozs7MzAIJwQxD4kHO6Zif\nZANal0gjIThvNI7yM4vSzhptnHt8fLT1em3Hx8dbM4/SBrDrAFWNL70SIqoh9gdyQwqfFHuViKjV\nasFKEMX8ZrNJDKH+559/rNvthqHnqL0iIr4XSkKo+lj3ZKvVCveXJyE4p5SE0AZfCvSqFtC781fq\nGz6PVjW1KiHSVIqaT/i5YsSSmrvTHMA8Fp0nq7NkISF+99kaSYg3Boe2yoaq1WpC1lqr1YLVCUmq\nDnuFoVO/9dgtl23oAXN8fByKE/V6PSghdBYEgTx/R+X3BPTqHz0YDELhAtlrRMTPIM0ujv3Kxa6D\n4PR8ghxDwkghbbPZxGTzwOATvV3Jnv89/T66NHXoHARVr9ezu7u70PVNRwn2Iq/x+nn0cwQI5nRv\nUxQm+PM+3U9PT4luUA0ItZgSybafByQEnXVpXUt+iDOD4vathNAkiH2lHUkU9MwsdLRrYWc6nSY6\nkubzeegWjng/UKJKu/Z0ELV2rJklZ9yoN7sOYv9RxVjE74PG79pJzue32WzC56fKCAhZMwv3EOSF\nFj+8fzUxly6zpFJLSQiKwGaWIB94VAICpYO3aCKvpSnl6ekpNM3RLMWcMJrnYqFx/9BGE2+/pU0n\nih8p/rP32Wd6VqU9D3Ej971aYfozUGdKxPzi9wPbGLUUx0KzVCqF7m3IKPXcRxmldjfE/vf39/Fs\niPghaHOH5nY8UpTn7uIOVIWpkq7aMKDKRSXsNVf0NT4eeW0Kf94p8cCiGVkb3jijlZTg/epdrIQF\ncSRkC++Phjy9m8mjaH7aR+4RSYg3BsoHEg8dIlKtVhM2DySpT09PgYEbDodBIpQVC4KIl6GDZDi0\nlKmlQ1I/f7Vg4vB4fn4OBT31UqSIF4f/RrwG1H7n9PR0iyCFhCC43Gw2YeCvevqPRqOQaEcS4rCQ\nVvSiq9IXQXxHGsEbazgcWq/Xs263G6xmGPClar639J6kADSbzUIASrBGkKpDESko+yGf+md6jxOo\n8n8XE6ifg87p4I4kkfXD1JSEyEJDhqpaIR2I7dSmRLuYHx4eEt3LfP29Qz8jDg9aYGafE/Mp+cC5\nSkEGu0NVdDEzZ18JY8T3gc8RNSBENjkgcb+/U82+nonkAL5D3Ow/6oVqtZo4P3TQ5mazScwiYak6\ng+f31mBpdky+A1MtHtiLm80m5Co3Nzd2fX0d8hWsLOI9uX+wLyjcabFu13wQ7cJVomLX5+kbWvTf\nVmWtty3zJIdaz3Ffkl9A4EX8PniVfLVaTQz+pQiLnRfkuVdtKRnrFWIREd8LVXPpgGqtv/mlCkFP\nyKqSWd0BmIOi9sRq46srzS3Az2sy2667VKtVazabVq/XrVAopLoT+BkYnkA2s60cnRyKvBylx83N\nTagp79M+O5IQbwxNVFFAtFqtRBG6WCyGi5cOFiUhKOjQZYxvV0Q2oQwth5t+/gxxoutIB/4qIwt7\nSWDP4UHHedwHEb8KLZDQmalKLTpdyuVyuMCRWutgYQrM2q0Ui2mHA7X8ILjyJETacECKD6z1em3D\n4dC63a59+fLFrq+v7e7uLnSCMtuGPfSWJARKCJJe7ZbB+s4seV5z7qq/uhbKKdKQbMU9/mvg/5bO\nukqlkrAuosjllRCj0Sjhhb4PpM2z8CREoVAIRWOScj8LxRcR4556X9DzRbv1NHnUzjWVzisJQQFH\nLVEisgklT6fTafBRh4BA/Zxma6ikty9CUGDx9ymzZLSL0w+7hNiADOWee6nwTNHDz4miaU5JlOVy\nGVT7t7e39unTJ7u7u0v4acdC4/7hB6K/NJvG7wWsgn2nr8J3/XoizQ/C5txL+7vcmaiuISGIH+N+\n+r1QJQRxDySEzoVRS1YIdB2A6+fYxDpGxI/CkwR6PynJ4OcipdkUan5Hrc4X/zebzdY9TGOJqhF0\nzoPaqut6fn5OKBp0nhzncBoJoUSEKiN4H8SRPhZYLpfBkQAr99vb22A1tc+7OZIQbww/AwICwish\n1M/z/v4+MQOAzaOS26iEyC7SZGJpSgj1Az49PTUzC124dF+OG+AAACAASURBVBKo5z6HhyYe8fKO\n+FWoJQqBZZoSgr1JYoASQokI7fqNRZLDgSohSDS4j/ysBl3a8UTXE8T5zc1N8H5VoiJNXfHawHef\nbnozC+cwhSCIXz2rda4OQZnvlCmVSgnLvJc6AiNehpIQ3JHaRaQdxWpLOJlMwj7aJwlBg4E2l2BP\ngKxaO9u1oOyHsv/ugXARvwdaDFYvfST32lWsn7/vAlYlRLTtyjY8CXF0dJSwuCT388UQMwuPWlzR\nQkc+n9+ap6QFPRbEuxY7dEi2DghWckELOX42lH6tnZ0PDw9bDVOfP3+2fr8fYoP1er3PjyTi/4PP\n8SUlhO+43WUbtwtpMxz031fyTf3Vv0VCkPdGJcR+wF320rxAiqCqYMV3noYLSAivsI6I+BF4JUQa\nEZGmhPAqCSVly+WyrVarLfXB/f391qBob/3k5zFAQuhAawr+aSozdVHReRG7CAhcdPx5qnk5jgDY\nuXe7Xbu+vrZut2uLxSKosKMS4p3CkxDtdjtIbpSEYAOQqKKE0IntWJzEAzvbSPMAVn99OiY1wKMA\nRsGCQwRPc1VCaDEvJqIRv4o0X3avhDg7O0tIaEmu1Y5pPB6bmSU6nyIOAxTAtBtT/Xe9VF69NAl2\nIEfxnLy+vrZ//vnHbm9vU7vi3nKPYMdE8ffx8TGcw5zFdG5xTnP2fo8dkw7c+1ZCHrEb+v8PCaE+\npkp6kswOBgObzWa/ZR+9hOPj44QSQhtMVAnBsDdNyj0RgbVXPDffH/xgarVjUiWEyuifn58Dqatd\nwH64a0Q2wf2wWCzCZ8v5xtB6rAIpaGjnI8W+5+fn1MGTSvpTaPDDq4+Pj7cKHRAPFHFRPnoLibTl\nOzzV6kGHUdOA8OnTJxsOh1tzACL2izQlhFdB7LL9UKuTl6CzHdLsmFQJoY0ozIZgfykRq2dgJCH2\nAyUhSqXSlh2TKiF0Hs50Og1xDyREVEZF/Ar0Xkoj0NMsmTh39M98flcul22z2WwV/R8eHraUD9zp\nupRMILYjbiOe417XBXmgCoaX7JggISD+dOk8KhqZ1Y7py5cvdnd3l2hGiEqIdwIv/6lUKkEBcXFx\nYR8+fLB2ux2GUeugV+0svru7C0NDkNrqRomHd3ZxcnJihUIhJBwUKLC1oWuAw8rsa9LCwF8ua728\n0ySMcR9E/ArS/KrZo1ooIfnUfUqROg6KO3z4YM4Pn1QpvtnX5JEuCwjz0Whk3W43Mb+IAuvvhO/E\n00IIe15tBdJIEv5fvKoN5SI/FxHfD7UeSRvORvFBbRr0vtOZHfuGdilpt6ielSQVWJUxXJiz0xNe\nEe8PJLl0HWvzkXr5KrHrZ6GQRGZhFkrEt6HdiNwRo9EoEN9PT0+2WCxCIYFHzg/tpIRsUusJbQ5Q\n0pbzhr3Ba6BAw69Vee9JCLWS5ZFuS31tWIJhl6cdzhobmsXGlCxBzxktQvnGC98cwKKRo1QqhXtY\n73W1AMvlctZsNu3i4sJarZZVq9WgAPM2dPy7PA97XZv5dhG4vK+It4Un1CGwlLzyMZGqXOv1ejgn\nOI80vksbWh4RkQZtglsul5bP50PdjLVcLhN/hzuO2pkn1TiHTk5OEhbDKJX1XPMWSqjs0+yg8vl8\n4rlUCUFuSR2Y79tsNomZTGpTa2aBbDCzLfICwpb64Xg8tuvra+v1ejYej4OazP/M7QORhHhF+EFg\nDBtpNBp2fn5ul5eX9vHjR2u321atVi2fz5uZJWx3+v2+DQYD63a7NhwOg1RfA4R4MGcbkBDVatVa\nrVZY2DSoDN9sWz4F+QAppT6YFC5i8SLitaB+1VykBJYElbDtJC5p8wIiDhfaHectQ0gw0qxDIM9R\n63W7Xbu9vbXhcGjz+XwvBMQu+I6SNGIljYTwnYOFQsHW6/V3WxNEfIUOpuT/VTsjzWyLgMjqfefl\n0r6r3RMQ2rlMJ2fsZn//0IIeFhYQ/UpkanEQ2xxvQzYejyMJcQBQNTP35Xg8DqTCYrGw4XCYUDjg\nq67kP8ULtURkLpcWNbzdIap5JRggDvT7ISF0Mb9CX5tXQZyengY7FSUhVKVDgTpLZ3ZEcv4XOaeS\nWmZJ9SfEKR3tdL9Xq9UtEuL4+DhhM1wqlazRaNjHjx/t/PzcarVaOPc0bkKFq3sMOzG6k9lnlUol\nQeCSm/DeIt4W/PxrPJx2btGsw1mwXC4Dcfn8/GylUinEQ9pg6c+6iIg06N1Dcb5arSYad+fz+VZe\nd3JyElTvSkKg3KIWovkHdQ7fIODtkVADqdJC56joGettEFFarNdrOz09tfV6nfj58ucdlouPj4/h\n54efJZx0xuOxTSYTG41GdnNzY71eL8yAyAIBYRZJiFeFdhWzMZl4riQEHfJnZ2d2dHQUZLvqp0kh\nBxIiXrKHg1wuZ8ViMZAQl5eX1m63gxICEsLMQsFCbScI9hgagwpCB/7GCzritZBmHeYvPhJptaHx\nVj0Rhwvf4UTwQxcGCYZZslivfpNYMKCImM/ney+UaYdeGgmhiog0AmLX/wudXFEJ8f3wg9a8x7gW\nJbjjlIDIQsCsSNtTfjicEhBpJEQWFB0Rbwude8LQQwpp6qOtyi1tSvED2SMJkX1oodd3LEJA0FWu\nj3R60wRAIc+ff8RgurzvNMp53Vv+eyEh9BwuFArBjtPMEoUSJV1RrT08PCRICD3bYt6aPagKQkks\n/cz8AGK1iaY5rlKphMIdd/fJyUmYE8AeajQadnFxYZ1Ox+r1ejj3dE9T4GOfaC2Fc5M5X+omoOpJ\nEPfa22FXU47v1ubPKLgeHR0l5hhBVrGfsHHyneoREbvAPbher8MZUKvVAgkBEeFzhlwuF3IKJSEg\nILClfsk+WBVjfnaSt3pKyxE1F2JBqKj6UO2fVAlBfIHNq58xNxqNbDgcBvUsX+OuQ2N7FnKqSEK8\nIrRgQSCJEqLdbtvFxYV9/PgxQVKYbSshbm9vg6XFbDYLXS3xUD4MoITAhsmTEAyT0QBM5fc6E0SV\nEOqDGeWKEa8B7Wx7SQnBPotKiPeJtDk2XgnBYEu1N1IlBIOoVQ6blULZt0gIM0stCHvLHU24ohLi\nx+A9XHVpIUOLZjr8NGv33S4SwiypbnxJCRHPzvcP7a5LU0Kk2TGpEkJnoSClp5MtIpugSMA5QJFh\nsViEu5QmNS3aemKC4qsvFmC9oMt3FavaCssTis5YJmHVpIUUis5mXxuq8vl8wsqQTnYKQfy7aQXt\niGzBKyF2kRBqx0Qn+2QyCSoICnXeygvlf7vdtlarFWZg1uv1oIQ4OTlJEHOPj4+JQjT3KkoIzrr1\neh1yaCUhzCzut9+EtHjYF0qxlzH7aiOnVjfch4VCIXzum80mUfCNcXXES6AhkkbezWZjk8kkEFtK\nQii4y5RspQaidpieJIAc0xjNP3caueBznV3Na5vNJkFAaJ7K72usyL1OjEjzH83sDKIeDAY2GAwS\nNu8QglloEogkxCvCDzjRQcTtdts6nY5dXl4m/g7dpEiulYSg6ykO8Mk+tOM2n88HCSuzQBqNxpYS\nQqWHFCy8BzDyewL8rBT1It4P/NBM9V3VjjcuX5JOTXJjR+9hg2AszY5JkwuzpF0OSojhcGjdbtc+\nf/6c8KXe13mVNlCTfY6EVu2YNOHRbjzvTaydKVEJ8ePwhXvtFlISwhMQWVT/eSsmtVlMK/SghvBF\nn4j3C9+YBAmhSggSS7Pk3tcZYcSD0+k07KkYC2YX6nMPVqtV4tfHx8eJ4ixfKzHB56y2dN7Ln/uW\nBibmzhCjebUEZxB/rgWPXC5nlUolkcfoOaXnt5kFFcR8Pg9Dg1WFEZE9+PNFVfZ81qpEKBaLdnR0\nZKvVKnQa1+t1m06nCdKAGLLT6Vin0wnqh2azmbBoQlHLa2H/8m/qc3KvPj8/2/Hxsa3X64RiiD2r\nP2uRnH077FJC+DyBHEFjPbOkNSGfnxaRd81rU/zoufKz51A8v7INzg6UhicnJ4m5uiw/T+7s7CyV\nJEBl4HM+HnGt0aX1D+7mNLW3H2jt1a/Ugb2aQhsEeH1eIa7xYb/ft36/b71ez3q9nt3d3QUiQmOF\nLOUekYR4RaiHIUFlo9GwarWa8EHUYPLx8XFrsKcWn5FdxwMxm6BjRAP5ZrNpjUYjEA8knb67fBcB\nwRR7PNzG43Gmuooj3hd2BZXa6Z3WmakKnaxcaBE/B09E+eHkvljvC8Ua4GShaKxWAqVSKXEX02Wa\nNlDPLGmRl/Z+vW9txI/Dq/l2SYOV3NfHfUDVGr5TlIYTHThMoqEWOz55iXHd+4OX4nOepg135QxC\nFc0Z42fv+L2TNUIu4sdB8QSiiWYP34zE/tFHX1ihoOxVDn7feFLi4eFha0YSBWe1T1F7Oe5H1Dl3\nd3d2c3NjNzc31u/3bTqd2mq1isXgjILmkcViEWJ8OoghkvAvf35+DkW0crlszWYzoZIxs0RBL5fL\nhdyXeIuiG1Zk7DtPiOkZ+fj4GAbFqjUUJG6r1bLz83ObTqd2dnYWCP64794WSkL6hh5dGifxd4iV\ntShL/MRniqWb/zy1Cc7bvHnLnJfsc3zhl9eoj2lWoPGuzSb4LPnslsuljcdj63a7gbRUAtRbDKvV\nsFcd5HI5u7+/TygQ0iwPdV8+PT2Fnw2tCeoe4pz0exUCjr/Pc+kys8R9v9lsbDQaBdVDt9u1u7u7\n1BpyVhXlkYR4RcDy0gVPMdr7v+rAws1mE9g7iIjhcGiTySTRVRKRTaiVDavZbIbPXkkI7TBX72gt\n8E6nUxuNRiGwH41GNp1Ow1CniIjXhO9soUOFi1mlziggIE2VhIiB/2EjTcWngzJfIiG0EKJB2T6D\nnZOTk8Qg2FarFc7iarUaLMe8KsLLwL/VnZ+lYO5QkEZApH0NskBA8O+rsgbSlnhP7Rb92amFZCWx\n4v55X1DlA0koxWOKbBChnEHqkc4+URJCO5fj3nlfoBir1kbL5TLRDOJnROC1rnfv4+NjsMjU5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7y+Uy5LTdbjfYMOH2oHvGg0YEOoWPjo5CJ3GlUrFarRaKdWrTxAwzZto1Go3w2mn89MNqI34O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f598ae6a250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_size = 10\n",
    "test_origin_img = mnist.test.images[0:test_size, :]\n",
    "test_reconstruct_img = np.reshape(x_reconstruct.eval(feed_dict = {x: test_origin_img}), [-1, 28 * 28])\n",
    "plot_n_reconstruct(test_origin_img, test_reconstruct_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這裡我們可以看到它重建的結果很不錯，尤其 mean square err 都低很多，可見 convolution 實在是滿強大的．接下來我們要輸入一個數字 (7) 看它在 code layer 經過 filter 之後的樣子是如何，這裡印出前 16 個結果．\n",
    "\n",
    "### Plot code layer result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "def plot_conv_layer(layer, image, num_filters):\n",
    "    output = sess.run(layer, feed_dict = {x: [image]})\n",
    "    \n",
    "    num_grids = int(math.ceil(math.sqrt(num_filters)))\n",
    "    \n",
    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
    "    \n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        if i < num_grids * num_grids:\n",
    "            img = output[0, :, :, i]\n",
    "            ax.imshow(img, interpolation='nearest', cmap='gray')\n",
    "        \n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mF2gAKE8ZAlCeMgSgPGUIQHnKEIDylCEA5SlDAMpThgCUpwwBKE8ZAlBe6usHnHW3jPMM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\nKE8ZAlCeMgSgPGUIQHnKEIDy/gc4y9neAwPsrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f598b1c8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(code_layer, image1, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到不是所有的 filter 輸出都有結果，很多都是完全是 0．而有值的輸出，可以隱隱約約看到有數字的形狀．\n",
    "\n",
    "\n",
    "## Max Unpooling\n",
    "\n",
    "那在 `Max Unpooling` 要如何實現呢？最簡單的想法是**怎麼來就怎麼回去**，encoder 在做 max pooling 的時候記下取 max 的索引值，而在 unpooling 的時候依據索引回填數值，其他沒有記錄到的地方則為零．\n",
    "\n",
    "使用 `tf.nn.max_pool_with_argmax` 這個函數，它除了會回傳 pooling 的結果外也會回傳對應原本的索引值 (argmax)，如下．\n",
    "\n",
    ">The indices in argmax are flattened, so that a maximum value at position [b, y, x, c] becomes flattened index ((b * height + y) * width + x) * channels + c.\n",
    "\n",
    "理論上在做 unpooling 的時就會用到這裡產生的對應表．不過目前 tensorflow 中沒有 unpooling 這個 op (可以參考 [issue](https://github.com/tensorflow/tensorflow/issues/2169))．因此以下展示了兩種方法作 unpooling 也都不會用到 argmax．\n",
    "\n",
    "1. 使用 Github Issue 討論中的方法，也就是放大兩倍後在固定的地方填值 (ex. 左上角)\n",
    "\n",
    "2. 借用影像的 upsample 函數 `tf.image.resize_nearest_neighbor` 來做等比例放大，也就不會補 0．\n",
    "\n",
    "註: \n",
    "- 在 encoder 中都是一個 convolutional layer 接一個 max pooling，因此 convolutional layer 的 strides 就調回為 1，只讓 max pooling 做降維．\n",
    "- 經過測試以後 encoder 使用 relu；decoder 使用 sigmoid，才有比較好的還原效果，都使用 relu 會訓練失敗．\n",
    "\n",
    "### Build helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding = 'SAME')\n",
    "\n",
    "def deconv2d(x, W, output_shape):\n",
    "    return tf.nn.conv2d_transpose(x, W, output_shape, strides = [1, 1, 1, 1], padding = 'SAME')\n",
    "\n",
    "def max_unpool_2x2(x, output_shape):\n",
    "    out = tf.concat_v2([x, tf.zeros_like(x)], 3)\n",
    "    out = tf.concat_v2([out, tf.zeros_like(out)], 2)\n",
    "    out_size = output_shape\n",
    "    return tf.reshape(out, out_size)\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "    _, argmax = tf.nn.max_pool_with_argmax(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding = 'SAME')\n",
    "    pool = tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    return pool, argmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build compute graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code layer shape : (?, 7, 7, 32)\n",
      "reconstruct layer shape : (?, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "x = tf.placeholder(tf.float32, shape = [None, 784])\n",
    "x_origin = tf.reshape(x, [-1, 28, 28, 1])\n",
    "\n",
    "W_e_conv1 = weight_variable([5, 5, 1, 16], \"w_e_conv1\")\n",
    "b_e_conv1 = bias_variable([16], \"b_e_conv1\")\n",
    "h_e_conv1 = tf.nn.relu(tf.add(conv2d(x_origin, W_e_conv1), b_e_conv1))\n",
    "h_e_pool1, argmax_e_pool1 = max_pool_2x2(h_e_conv1)\n",
    "\n",
    "W_e_conv2 = weight_variable([5, 5, 16, 32], \"w_e_conv2\")\n",
    "b_e_conv2 = bias_variable([32], \"b_e_conv2\")\n",
    "h_e_conv2 = tf.nn.relu(tf.add(conv2d(h_e_pool1, W_e_conv2), b_e_conv2))\n",
    "h_e_pool2, argmax_e_pool2 = max_pool_2x2(h_e_conv2)\n",
    "\n",
    "code_layer = h_e_pool2\n",
    "print(\"code layer shape : %s\" % code_layer.get_shape())\n",
    "\n",
    "W_d_conv1 = weight_variable([5, 5, 16, 32], \"w_d_conv1\")\n",
    "b_d_conv1 = bias_variable([1], \"b_d_conv1\")\n",
    "\n",
    "# convolutional layer 不改變輸出的 shape\n",
    "output_shape_d_conv1 = tf.pack([tf.shape(x)[0], 7, 7, 16])\n",
    "h_d_conv1 = tf.nn.sigmoid(deconv2d(code_layer, W_d_conv1, output_shape_d_conv1))\n",
    "\n",
    "# max unpool layer 改變輸出的 shape 為兩倍\n",
    "output_shape_d_pool1 = tf.pack([tf.shape(x)[0], 14, 14, 16])\n",
    "h_d_pool1 = max_unpool_2x2(h_d_conv1, output_shape_d_pool1)\n",
    "\n",
    "W_d_conv2 = weight_variable([5, 5, 1, 16], \"w_d_conv2\")\n",
    "b_d_conv2 = bias_variable([16], \"b_d_conv2\")\n",
    "\n",
    "# convolutional layer 不改變輸出的 shape\n",
    "output_shape_d_conv2 = tf.pack([tf.shape(x)[0], 14, 14, 1])\n",
    "h_d_conv2 = tf.nn.sigmoid(deconv2d(h_d_pool1, W_d_conv2, output_shape_d_conv2))\n",
    "\n",
    "# max unpool layer 改變輸出的 shape 為兩倍\n",
    "output_shape_d_pool2 = tf.pack([tf.shape(x)[0], 28, 28, 1])\n",
    "h_d_pool2 = max_unpool_2x2(h_d_conv2, output_shape_d_pool2)\n",
    "\n",
    "x_reconstruct = h_d_pool2\n",
    "print(\"reconstruct layer shape : %s\" % x_reconstruct.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(tf.pow(x_reconstruct - x_origin, 2))\n",
    "optimizer = tf.train.AdamOptimizer(0.01).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, loss 0.143627\n",
      "step 100, loss 0.0930532\n",
      "step 200, loss 0.0884409\n",
      "step 300, loss 0.0928427\n",
      "step 400, loss 0.0909152\n",
      "step 500, loss 0.0826991\n",
      "step 600, loss 0.0848275\n",
      "step 700, loss 0.0810827\n",
      "step 800, loss 0.0864824\n",
      "step 900, loss 0.086182\n",
      "step 1000, loss 0.0865941\n",
      "step 1100, loss 0.0828469\n",
      "step 1200, loss 0.091879\n",
      "step 1300, loss 0.0909268\n",
      "step 1400, loss 0.0917193\n",
      "step 2000, loss 0.0869293\n",
      "step 3000, loss 0.078727\n",
      "step 4000, loss 0.0829563\n",
      "final loss 0.0858374\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "batch_size = 60\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)\n",
    "\n",
    "for epoch in range(5000):\n",
    "    batch = mnist.train.next_batch(batch_size)\n",
    "    if epoch < 1500:\n",
    "        if epoch%100 == 0:\n",
    "            print(\"step %d, loss %g\"%(epoch, cost.eval(feed_dict={x:batch[0]})))\n",
    "    else:\n",
    "        if epoch%1000 == 0: \n",
    "            print(\"step %d, loss %g\"%(epoch, cost.eval(feed_dict={x:batch[0]})))\n",
    "    optimizer.run(feed_dict={x: batch[0]})\n",
    "    \n",
    "print(\"final loss %g\" % cost.eval(feed_dict={x: mnist.test.images}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot recontructed images\n",
    "\n",
    "可以看到重建的影像有成功，但是有點狀的稀疏情形，因為在經過 unpooling 的時候使採取補 0 的動作．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4qpSLxcITDDmnF9I9bPmV6NuB7PnQXm+sMuCbnZuNDnQtFgsMBgO0223c39/j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JRsNiuJREKtVxHkQ6UWbF1x3rMZ4m7Skbv32z2Z6xx5bZD4OZ/PtZH6YDCQbrcr7XZbms2mFItF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ux4eoCbfj42OcnJyg1+shGAyKg+cVwKjjU7TcrgYWOVxMBm9vb+P09BQfP35EIBCQ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\nomfBJAQRERERERERERERET2L/wG0Szk8AlYbxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2030f9a290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_size = 10\n",
    "test_origin_img = mnist.test.images[0:test_size, :]\n",
    "test_reconstruct_img = np.reshape(x_reconstruct.eval(feed_dict = {x: test_origin_img}), [-1, 28 * 28])\n",
    "plot_n_reconstruct(test_origin_img, test_reconstruct_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot code layer result\n",
    "\n",
    "而在 code layer 中會發現也是部分的 filter 有值，另外許多部分都為 0．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2030e46210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(code_layer, image1, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下來觀察 deconvolution 1 的輸出，以及經過 unpooling 後的 pool 1 輸出，會很明顯地看到 unpooling 輸出有等比例放大，但是會有稀疏的情形，這就是因為補 0 的緣故．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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30ksvqZktW7YEx62vTxG+GQIAQBkCAEAZAgDcowwBAO5RhgAA9yhDAIB7lCEAwD3KEADg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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2034d1d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(h_d_conv1, image1, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jPW6tMAN91h891h830AAA4AnDEABgPIYhAMB4HLr38rF0xaF7M5SUlFj6HBwc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\n3ETQSjPiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2030e68b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(h_d_pool1, image1, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下來使用 `tf.image.resize_nearest_neighbor` 的方法來做還原\n",
    "\n",
    "### Build helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def conv2d(x, W):\n",
    "    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding = 'SAME')\n",
    "\n",
    "def deconv2d(x, W, output_shape):\n",
    "    return tf.nn.conv2d_transpose(x, W, output_shape, strides = [1, 1, 1, 1], padding = 'SAME')\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "    _, argmax = tf.nn.max_pool_with_argmax(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding = 'SAME')\n",
    "    pool = tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')\n",
    "    return pool, argmax\n",
    "\n",
    "def max_unpool_2x2(x, shape):\n",
    "    inference = tf.image.resize_nearest_neighbor(x, tf.pack([shape[1]*2, shape[2]*2]))\n",
    "    return inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build compute graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "code layer shape : (?, 7, 7, 32)\n",
      "reconstruct layer shape : (?, 28, 28, ?)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "x = tf.placeholder(tf.float32, shape = [None, 784])\n",
    "x_origin = tf.reshape(x, [-1, 28, 28, 1])\n",
    "\n",
    "W_e_conv1 = weight_variable([5, 5, 1, 16], \"w_e_conv1\")\n",
    "b_e_conv1 = bias_variable([16], \"b_e_conv1\")\n",
    "h_e_conv1 = tf.nn.relu(tf.add(conv2d(x_origin, W_e_conv1), b_e_conv1))\n",
    "h_e_pool1, argmax_e_pool1 = max_pool_2x2(h_e_conv1)\n",
    "\n",
    "W_e_conv2 = weight_variable([5, 5, 16, 32], \"w_e_conv2\")\n",
    "b_e_conv2 = bias_variable([32], \"b_e_conv2\")\n",
    "h_e_conv2 = tf.nn.relu(tf.add(conv2d(h_e_pool1, W_e_conv2), b_e_conv2))\n",
    "h_e_pool2, argmax_e_pool2 = max_pool_2x2(h_e_conv2)\n",
    "\n",
    "code_layer = h_e_pool2\n",
    "print(\"code layer shape : %s\" % code_layer.get_shape())\n",
    "\n",
    "W_d_conv1 = weight_variable([5, 5, 16, 32], \"w_d_conv1\")\n",
    "b_d_conv1 = bias_variable([1], \"b_d_conv1\")\n",
    "\n",
    "# convolutional layer 不改變輸出的 shape\n",
    "output_shape_d_conv1 = tf.pack([tf.shape(x)[0], 7, 7, 16])\n",
    "h_d_conv1 = tf.nn.sigmoid(deconv2d(code_layer, W_d_conv1, output_shape_d_conv1))\n",
    "\n",
    "# max unpool layer 改變輸出的 shape 為兩倍\n",
    "h_d_pool1 = max_unpool_2x2(h_d_conv1, [-1, 7, 7, 16])\n",
    "\n",
    "W_d_conv2 = weight_variable([5, 5, 1, 16], \"w_d_conv2\")\n",
    "b_d_conv2 = bias_variable([16], \"b_d_conv2\")\n",
    "\n",
    "# convolutional layer 不改變輸出的 shape\n",
    "output_shape_d_conv2 = tf.pack([tf.shape(x)[0], 14, 14, 1])\n",
    "h_d_conv2 = tf.nn.sigmoid(deconv2d(h_d_pool1, W_d_conv2, output_shape_d_conv2))\n",
    "\n",
    "# max unpool layer 改變輸出的 shape 為兩倍\n",
    "h_d_pool2 = max_unpool_2x2(h_d_conv2, [-1, 14, 14, 1])\n",
    "\n",
    "x_reconstruct = h_d_pool2\n",
    "print(\"reconstruct layer shape : %s\" % x_reconstruct.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(tf.pow(x_reconstruct - x_origin, 2))\n",
    "optimizer = tf.train.AdamOptimizer(0.01).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0, loss 0.117823\n",
      "step 100, loss 0.022254\n",
      "step 200, loss 0.0202093\n",
      "step 300, loss 0.0189887\n",
      "step 400, loss 0.0180246\n",
      "step 500, loss 0.0189967\n",
      "step 600, loss 0.0178063\n",
      "step 700, loss 0.0176837\n",
      "step 800, loss 0.0180569\n",
      "step 900, loss 0.0173936\n",
      "step 1000, loss 0.0176715\n",
      "step 1100, loss 0.0180319\n",
      "step 1200, loss 0.0170217\n",
      "step 1300, loss 0.0181365\n",
      "step 1400, loss 0.0164201\n",
      "step 2000, loss 0.0178452\n",
      "step 3000, loss 0.0171906\n",
      "step 4000, loss 0.0164161\n",
      "final loss 0.0172661\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "batch_size = 60\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)\n",
    "\n",
    "for epoch in range(5000):\n",
    "    batch = mnist.train.next_batch(batch_size)\n",
    "    if epoch < 1500:\n",
    "        if epoch%100 == 0:\n",
    "            print(\"step %d, loss %g\"%(epoch, cost.eval(feed_dict={x:batch[0]})))\n",
    "    else:\n",
    "        if epoch%1000 == 0: \n",
    "            print(\"step %d, loss %g\"%(epoch, cost.eval(feed_dict={x:batch[0]})))\n",
    "    optimizer.run(feed_dict={x: batch[0]})\n",
    "    \n",
    "print(\"final loss %g\" % cost.eval(feed_dict={x: mnist.test.images}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot reconstructed images\n",
    "\n",
    "Bingo!可以看到效果算不錯的重建影像，而且不會有前一種方法的稀疏情形．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4qpSLxcITDDmnF9I9bPmV6NuB7PnQXm+sMuCbnZuNDnQtFgsMBgO0223c39/j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yq1YOuK857NEHeTjty93+7JXOfIW4PEz/l8ro3UB4OBdLtdabfb0mw2pVgsaqwG3v9Yo7PZrJRK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\n2O12WC6X7L0eHh4wn8+xXq+RJInatOqQCCHB55fjHCfe8zyEYYjpdIp37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kLE9fU1ptMp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59873be4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_size = 10\n",
    "test_origin_img = mnist.test.images[0:test_size, :]\n",
    "test_reconstruct_img = np.reshape(x_reconstruct.eval(feed_dict = {x: test_origin_img}), [-1, 28 * 28])\n",
    "plot_n_reconstruct(test_origin_img, test_reconstruct_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot code layer result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59873a8f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(code_layer, image1, 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到 convolutional layer 的輸出在 unpooling 中被很平滑的放大兩倍．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5978de9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(h_d_conv1, image1, 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1fvWrX/XXQzIzs/b2dil35513upns7OzgeE1NjTRXqlq4cKFNmjQpmHnttdfcOvfff780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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f59873a8f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = mnist.test.images[0]\n",
    "plot_conv_layer(h_d_pool1, image1, 16)"
   ]
  },
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   "source": [
    "## 小結\n",
    "\n",
    "這裡實現了 convolutional autoencoder，包含使用了 `deconvolution` 以及 `max unpooling` 兩個方法來組成 decoder．其中 deconvolution 使用了官方的 op，而 max unpooling 則使用了兩種非官方的方法，其中用 tf.image.resize_nearest_neighbor 的方法所做的 unpooling 效果較好．\n",
    "\n",
    "遇到最困難的點會是一開始在 decoder 都使用 relu 作 activation function，但是完全得不出好的重建影像，而後來改用 sigmoid 後才成功．我想是因為 relu 會讓小於 0 的部分都等於 0，失去了影響後面網路的能力．或許在更大且複雜的網路，或是較長的訓練時間，才有可能成功．\n",
    "\n",
    "### 問題\n",
    "\n",
    "- conv2d_transpose 是如何利用 padding 調整輸出大小的呢?\n",
    "- convolutional autoencoder 和 autoencoder mean square error 似乎低很多，why?\n",
    "\n",
    "## 學習資源連結\n",
    "\n",
    "- [github convolutional autoencoder example](https://github.com/pkmital/tensorflow_tutorials/blob/master/python/09_convolutional_autoencoder.py)\n",
    "- [tensorflow conv2d_transpose doc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.nn.conv2d_transpose.md)\n",
    "- [tensorflow max pool with argmax doc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.max_pool_with_argmax.md)\n",
    "- [max unpool implement](https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation/blob/master/tests/UnpoolLayerTest.ipynb)"
   ]
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